Building chat infrastructure for 3M users on shaky 3G: architecture decisions we made with Kupu


Everyone knows big names like LinkedIn, Indeed, and Glassdoor. But in Southeast Asia, a new wave of phone-first apps is taking over. This follows the massive success of "Direct Chat" apps in China, like Boss Zhipin (China's leading direct-talk recruitment app).

Now, platforms like Kupu are changing how millions of people find work in Indonesia. In today's fast world, the gig economy is evolving... rapidly. Traditional emails are just too slow and boring. Honestly, the modern HR Tech revolution is all about talking to people right away. This is why these apps need a world-class Chat SDK and a super-steady In-app Chat API. They turn every job post into real, live talk.

Indonesia is a extraordinarily complex puzzle with 17,000 islands. Finding a job for a waiter or a rider in that mess is a giant headache. For years, folks were stuck with paper resumes and old job boards.

But now, Indonesia is going through a big digital change. We are seeing a clear trend here. Recruitment is moving away from just "posting an ad." It is now about "starting a conversation." In this busy world, being able to chat instantly is the most important part of getting a job.

Kupu is already a leader with over 3 million downloads. The team behind Kupu is very smart. They have deep experience from tech giants like Alibaba and Lazada.

Why is Kupu winning? It's because they focus on "Direct Chat." They eliminated traditional paper resumes. Instead, they use smart AI profiles and short videos. Most importantly, the chat is the center of the whole app.

A candidate can message a hiring manager right away. This "Chat-First" model is a game-changer. It cuts the time it takes to hire someone from weeks down to just a few days. This approach proves highly effective in making job hunting as easy as sending a message on a social app is a genius move. Now, millions of users and employers trust the platform because it gets results fast.

Whether it's a rider waiting for the next delivery, a cleaner booking their next shift, or a warehouse worker finding a new role -- on-demand services and blue-collar recruitment share the same need: instant, reliable communication.

While Kupu's idea was brilliant, making it work perfectly across 17,000 islands was a technical nightmare. In a recruitment app, a lost message is a lost life-changing opportunity. Kupu faced several "hard truths" when building their communication system.

The Network Gap and Weak Signals

In a big city like Jakarta, 4G is everywhere. Lots of users are in warehouses or far-off islands where the net is highly unstable. Job hunting needs a rock-solid chat. If the signal drops for hours, the Chat SDK must automatically reconnect as soon as network availability is restored. No messages can just vanish. Trust me, losing a job invite because of a shaky signal is a total nightmare.

The "Silent Killer" of Job Opportunities: Push Delivery

In Indonesia, there is a "Silent Killer": Push Delivery. Most workers use cheap Android phones. These phones close apps in the background to save battery life. Honestly, without a 99.9% push rate, the whole app becomes unreliable.

Trust and Security in the Chat Window

Recruitment is a sensitive business. It involves phone numbers, home addresses, and personal history. In the blue-collar market, scammers often try to use chat to trick workers. Kupu needed a way to keep their users safe without slowing down the conversation. They required a system that could handle encrypted messaging and support content moderation tools to flag suspicious links or fake job offers in real-time.

To overcome these hurdles, Kupu turned to Nexconn. With over ten years of experience in the global communication market, Nexconn provided an Enterprise Messaging API and a specialized Chat SDK designed specifically for high-stakes business communication.

Global Network, Local Speed

Nexconn's SD-CAN network (Software Defined Communication Accelerate Network) is the backbone of this solution. While Nexconn operates 8 major data centers around the world, they placed a heavy focus on Southeast Asia PoPs (Points of Presence). By keeping the servers close to Indonesian users, Nexconn ensured "Zero-Feel Lag".

Guaranteed Delivery on Any Device

Nexconn solved Kupu's biggest headache -- message delivery on cheap phones. The Nexconn In-app Chat API is built to work with all major phone manufacturers. Even when the Kupu app is not actively running, Nexconn's smart delivery system ensures the message reaches the user through the best possible channel. Furthermore, for the weak internet areas of Indonesia, Nexconn used its specialized protocols to recover lost data packets. Even on a shaky 3G connection, the chat stays connected.

Smart Business Features

Beyond just sending text, Nexconn enabled Kupu to use "Custom Card Messages." This allows employers to send official interview invites or job offers as interactive cards within the chat. By using the Nexconn Enterprise Messaging API, Kupu can track exactly when a message was "Delivered" and "Read," giving employers the data they need to manage their hiring funnel.

Nexconn understands that every app has different needs. To help Kupu stay light, Nexconn offers its Chat SDK in two different "flavors."

First, there is the Chat SDK. This is the "pure" version of the tech. It only contains the heavy-duty engine needed for communication -- like sending messages, managing chat lists, and organizing data. It has zero UI elements, making it incredibly tiny. For a professional app like Kupu, which has its own unique design, Chat SDK is the perfect choice. It lets their designers create their own "look" while using Nexconn's powerful In-app Chat API under the hood. It's all the power without any extra weight.

Second, for apps that want to launch fast, Nexconn offers Chat UI. This version comes with pre-made chat windows, contact lists, and message bubbles. It's like a "ready-to-go" chat room that you can drop into your app in a day. While it's a bit larger than Chat SDK, it saves developers hundreds of hours of design work. Honestly, this kind of choice is rare. It means whether you are a giant like Kupu or a small startup, Nexconn's Enterprise Messaging API scales perfectly with your goals. By offering this "pick-and-choose" style, Nexconn ensures the app stays as lean as possible for the millions of workers in Indonesia.

Trust is Everything: Nexconn's Smart Content Moderation

Nexconn offers a powerful layer of safety through its global Content Moderation services. This is a must-have for any serious Enterprise Messaging API. Nexconn's Content Moderation services can automatically scan for fake job links, rude language, or suspicious phone numbers in real-time. By filtering out bad content before it even reaches the user, Nexconn helps platforms build a "Circle of Trust." Honestly, in a market as big as Indonesia, you can't just hope users stay safe; you need a smart, automated system to guard the gate 24/7. This ensures that every In-app Chat remains a professional and secure space for real job seekers.
 
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  • It depends on the qualities you are looking for. if they job requires a less confident person, dont hire him

  • Of course you can't judge judge someone's entire because of a day of interview, you may miss a very objective person who would help challenge and grow... your company, I would rather do a probation recruitment  more

  • Two things can be true: you do get along with coworkers, and that team had a different view of “team player.” Blunt rejections often say more about... the messenger than you.

    If you want to stay where you are, you don’t have to become someone else overnight. Maybe ask a trusted colleague or manager for 1-2 specific examples. Then you’ll know if it’s real, or just that team’s bias. You’ve got options either way.
     more

  • When you're around people like that you never show them that you want something better for yourself never you always have to just keep a smile and... show them that you fake I'm not fake but I can show you fake but I'm really not that and like you just wait until your turn and they'll come on ask you do you want that position you know what I'm saying you got to do it that way to where you're not paying them attention only when they need the attention. more

AI Agents Won't Replace Your Job -- But Ignoring Them Might


By early 2026, the pitch has become unavoidable: build an AI agent, hand it your job, collect the output. Creators on every platform are packaging this idea as a survival strategy -- automate your role before someone else automates you out of it. The tools feeding this narrative are real. n8n, Make, and a growing stack of LLM APIs have made it genuinely possible for a non-engineer to wire together... a multi-step reasoning pipeline in an afternoon. That accessibility is new, and it matters.

The problem isn't the tools. It's the framing. "Replace your job with an agent" conflates two very different things: automating the tasks inside a job versus automating the judgment that makes those tasks worth doing. Those are not the same thing, and treating them as equivalent leads to expensive, embarrassing failures. McKinsey's research on the future of work makes this distinction clearly -- organizations that invest in AI capabilities while reskilling their workforce outcompete those that treat automation as a headcount substitution strategy (McKinsey, Future of Work). The word "while" is doing a lot of work in that sentence.

The strongest version of this argument goes like this: most knowledge work is pattern-matching dressed up as expertise. A sales rep qualifies leads by checking a list of criteria. A recruiter screens résumés against a job description. A content writer produces variations on proven formats. If the task is pattern-matching, a well-prompted reasoning model can do it faster, at higher volume, and without sick days.

This is not wrong. I've watched pipelines built in n8n handle lead research, scoring, and first-draft outreach in a single automated chain -- work that previously occupied hours of a junior SDR's week. The throughput gains are real. When we built our first Autonomous SDR pipeline, a flat three-component architecture -- research, scoring, and writing all reporting to a single orchestrator -- worked fine at five leads. At fifty, the scoring module sat idle waiting on research that had nothing to do with scoring. Splitting into discrete components with explicit handoff contracts between them cut end-to-end processing time and made each stage independently testable. That architectural lesson applies whether you're building for yourself or for a client.

So yes: if your job is mostly execution of repeatable, well-defined tasks, a well-built automation chain can absorb a meaningful portion of it. That's not hype. That's just what these tools do.

The limit appears the moment the task requires something the pipeline can't define in advance. Negotiating a contract renewal when the client is upset. Deciding which of two technically correct answers is politically safe to give. Recognizing that a prospect's question means something different than what they literally asked. These aren't edge cases -- they're the core of most senior roles. No current LLM handles them reliably, and pretending otherwise is how you ship a customer-facing system that embarrasses your company.

The augmentation argument is less viral but more defensible. Instead of asking "what tasks can I remove from my job," it asks "what tasks are consuming time I should be spending on higher-judgment work?" The pipeline handles the former. The person handles the latter.

This reframing changes what you build. An agent that drafts ten cold email variations for a human to review and select is a different system than one that sends them autonomously. The first one makes the human faster. The second one removes the human -- and with them, the judgment about which variation fits the specific relationship context that no CRM field captures.

Practically, augmentation pipelines are also more maintainable. Autonomous systems require monitoring, error handling, fallback logic, and someone who notices when the output quality degrades. That's not passive income -- it's a second job. I've seen founders build elaborate n8n workflows to automate their outreach, then spend more time debugging the automation than the original task took. The maintenance burden is real, and it scales with complexity. Our post on cold email automation system design goes into the specific failure modes that catch people off guard.

Augmentation also preserves the accountability structure that clients and employers actually care about. When an autonomous pipeline makes a mistake -- and it will -- the question "who approved this?" has no good answer. When a human uses a pipeline to do their work faster, the answer is obvious. That accountability matters more than most automation advocates acknowledge.

The practical question isn't philosophical. It comes down to three variables: task definition clarity, error cost, and output reviewability.

Automate fully when: the task has a clear, stable definition (the inputs and acceptable outputs don't change week to week); the cost of a wrong output is low or easily caught downstream; and you can review a sample of outputs without it taking longer than the task itself. Data enrichment, calendar scheduling, invoice parsing, and first-draft content generation often meet all three criteria.

Keep a human in the loop when: the task definition shifts based on context you can't encode in a prompt; a wrong output damages a relationship, triggers a legal issue, or ships to a customer; or the review process requires the same judgment as the original task. Client communication, contract decisions, and anything touching regulated data typically fail at least one of these tests.

There's a third category worth naming: tasks that look automatable but aren't yet. Competitive analysis, for instance. A reasoning model can summarize a competitor's pricing page. It cannot tell you whether that pricing change signals a strategic pivot or a desperate response to churn. That distinction requires market context, relationship knowledge, and pattern recognition built over years. Automating the summary is useful. Automating the interpretation is dangerous.

We explored this tension directly when comparing manual research processes to AI-assisted ones -- the grant research automation analysis is a good case study in where the line actually sits in practice.

The viral framing skips the maintenance math. Building a working automation pipeline in n8n or a similar orchestration tool takes real time -- not because the tools are hard, but because the edge cases are endless. What happens when an API returns a malformed response? When a lead's LinkedIn profile is private? When the LLM produces output that's technically valid but contextually wrong?

Every one of those scenarios needs a handler. And the handlers need testing. And the tests need updating when the upstream API changes its schema. This is engineering work, not content creation. Treating it as a passive asset that runs indefinitely without attention is how you end up with a pipeline that's been silently failing for three weeks.

The honest version of "build agents to replace your job" is: build agents to handle the parts of your job that don't require your judgment, then use the recovered time to do more of the work that does. That's a real productivity gain. It's just not as shareable as "I automated my entire income stream."

For a grounded look at what building eighty automations without a traditional engineering background actually produces -- including what breaks -- our post on building automations without code covers the real results, not the highlight reel.

Start with a task audit, not a tool selection. Before touching n8n, Make, or any LLM API, I'd spend a week logging every task I do and tagging each one: "stable definition / low error cost / reviewable output" or not. Most people skip this and build pipelines for tasks that feel automatable but fail the error-cost test in production. The audit takes a few hours. Rebuilding a broken autonomous system takes weeks.

Build the human-in-the-loop version first, always. Even if the goal is full automation, ship the version where a person reviews outputs before they go anywhere. Run it for two weeks. The failure modes you discover in that period will reshape the architecture entirely -- and you'll catch them before they reach a customer or a client. We've never regretted this sequencing. We've regretted skipping it.

Price the maintenance before you celebrate the build. The next thing I'd do differently is attach a recurring time estimate to every pipeline before calling it done. If keeping this system accurate and functional requires four hours a month, that's the real cost of ownership. Sometimes that math still favors automation. Sometimes it doesn't. Knowing in advance is the difference between a productivity tool and a liability.
 
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1   
  • Follow up with a thank you to the people who interviewed you, to stay in contact. It could mean many things: Some organizations take awhile in the ... interview process, and also awhile to get back to you. Meanwhile, continue your job hunt. more

    1
  • Pray about it , so your file does not get lost or be disconsidered.
    Once you get to the interview, another strategy is to be upfront: make a summary... of your strategies (what you said in the interview), put it in a beautiful slide, and share it with HR as a way to professionally reconnect with them by saying:
    "Thank you for the opportunity you offered to attend the last interview. Here is a summary of my responses and strategy."
    Something like that.
    God be with you.
     more

3   
  • stop looking for job. You should be trading your skill set for money. Someone needs your skills to progress their agenda.

  • Entrepreneurship Training would best help you and I recommend you find your passion and gain confidence in your abilities and allow yourself to step... out of your comfort zone maybe you need to change your environment within and allow it to show you who suppose to help you on the journey more

One-On-One Coaching Explained: A Complete Guide For Managers


What Is One-On-One Coaching? Definition, Benefits, And Best Practices For Managers

In recent years, one of the most popular trends in employee training has been personalized learning. The main reason for that is that in a highly volatile and competitive business environment, there really isn't much time for one-size-fits-all training programs. What employees truly need is individualized growth... pathways that align with their unique strengths, needs, and ambitions. This is where one-on-one coaching comes in.

But what is one-on-one coaching, and how can managers use it effectively in the workplace? This guide breaks down everything you need to know, from core principles and benefits to best practices and must-know steps for successful implementation, equipping you with the necessary knowledge to create meaningful coaching experiences that drive engagement and organizational success.

One-on-one coaching is a highly effective approach to employee development that provides tailored support and individualized attention. Specifically, it is a structured process where a manager (or coach) works directly with an employee to help them improve their performance, build skills, and support their professional growth. Unlike traditional training programs, which offer generic content, this method focuses on the unique needs, challenges, and goals of each employee. Coaches engage in dynamic conversations that combine instruction, active listening, and feedback, fostering an atmosphere where employees feel valued and motivated.

Such personalized coaching enables the creation of action plans that align with the employee's career goals, allowing for regular progress assessment and strategy adaptation. Additionally, it addresses immediate performance issues while promoting long-term career development. Finally, one-on-one coaching helps build essential soft skills such as communication and leadership, enhancing team dynamics and organizational performance.

These characteristics make one-on-one coaching a powerful tool for fostering personal and professional growth in employees.

The main reasons managers leverage one-on-one coaching in the workplace include driving employee engagement, improving performance, and fostering a culture of continuous learning. Rather than relying solely on formal training sessions, organizations are integrating coaching into everyday workflows. Managers act as coaches, helping employees navigate challenges, develop new competencies, and stay aligned with organizational goals.

The main use cases for this training approach usually include:

Although one-on-one coaching and training are often used interchangeably, these terms don't refer to the same learning approach. In this table, we have accumulated their main differentiating factors in terms of focus, approach, role of the manager, flexibility, and outcome.

In practice, the two approaches can complement each other. For example, a manager might use one-on-one training to teach a specific tool, then follow up with coaching sessions to reinforce application and growth.

We have already briefly mentioned the benefits of leveraging one-on-one coaching, but let's take a closer look at some of the most important ones.

One-on-one coaching provides personalized feedback tailored to each employee's unique strengths and weaknesses. This individualized approach helps employees clearly understand their performance metrics and pinpoint specific areas for improvement. By addressing these areas with constructive feedback and guidance, employees can enhance their skills and consistently achieve better results. This not only fosters a culture of accountability but also drives overall organizational effectiveness.

When employees feel that their voices are heard and their contributions are valued, their motivation and commitment to their work naturally increase. One-on-one coaching allows managers to actively listen to their employees' concerns and aspirations, which can lead to a stronger emotional connection to the workplace. Engaged employees are more likely to go above and beyond in their roles, resulting in higher productivity levels and a positive work environment.

One-on-one coaching sessions provide focused attention, allowing employees to engage in skill development much more effectively compared to traditional group learning formats. This is due to the fact that, in these sessions, employees can receive tailored training that addresses their specific learning styles and needs. This customized approach accelerates their ability to grasp new concepts, apply them in real-world situations, and retain the information effectively, leading to quicker mastery of essential skills.

Another significant benefit of one-on-one coaching is that they significantly improve rapport between managers and their employees. This is achieved through regular one-on-one meetings that foster an environment of trust, transparency, and open communication. These interactions allow managers to understand their team members on a deeper level, leading to more meaningful relationships. As managers demonstrate consistent support and recognition, employees will feel valued and appreciated, strengthening their loyalty and commitment to the team and the organization as a whole.

Organizations that invest in the development of their employees through one-on-one coaching essentially focus on creating pathways for career advancement. Employees are more likely to remain with an organization that demonstrates a commitment to their professional growth. By facilitating ongoing development opportunities, managers showcase their dedication to the success of their employees, leading to reduced turnover rates and a more stable workforce. As a result, this investment not only benefits individual employees but also enhances the organization's reputation as a supportive and nurturing workplace.

Now that we understand why managers would want to leverage one-on-one coaching in the workplace, it is time to explore how they can do it best. Let's discuss the 7 must-know steps to effectively implement one-on-one coaching sessions.

Before each session, it's vital to establish clear and specific objectives. This could involve identifying key focus areas such as solving a specific problem, enhancing a skill set, or evaluating progress towards individual and team goals. Having defined objectives creates a framework for the conversation and helps ensure that both you and your employee stay on track. Communicate your intentions clearly and invite your team member to share their own goals for the session.

Preparation is key to running a successful coaching session. Review notes from previous discussions and analyze any available performance data. Understanding where your employee currently stands will enable you to tailor your coaching approach to their specific needs. Encourage your employees to come prepared as well, suggesting they bring topics they wish to discuss, challenges they face, or feedback their peers may have given. This collaborative preparation fosters engagement and ownership of their development.

One of the most critical aspects of effective coaching is ensuring a psychologically safe space. Employees should feel comfortable and secure to express their thoughts, share any challenges they are facing, or ask questions, without fearing any consequences from peers or supervisors. For this to happen, managers and leadership must engage in actively listening, show empathy, and respond without judgment. Fostering these leadership skills will help you establish a work environment that encourages open dialogue and builds trust, which are essential for fostering transparency and facilitating growth.

Utilizing open-ended questions is a powerful technique during coaching sessions. Questions such as "What challenges are you currently facing?" or "What support do you need from me?" promote deeper reflection and critical thinking, instead of giving employees a few stock answers to choose from. Encourage your employees to think about what success looks like to them in various situations. This will not only help them clarify their thoughts but also allow you to guide them effectively towards solutions.

Constructive feedback is essential for employee growth. When delivering feedback, be specific about the behaviors and outcomes you're addressing to promote clarity rather than confusion. Use examples and present observations in a way that emphasizes the desired direction instead of merely pointing out mistakes. Remember that the ultimate goal is to balance constructive criticism with positive reinforcement, which is why you must highlight achievements and strengths in addition to discussing challenges. This balanced approach boosts morale and encourages a growth mindset.

It is important for one-on-one coaching sessions to feel like a two-way interaction. For this reason, it is essential that you collaborate on a clear action plan at the end of each meeting. This should include specific next steps, timelines, and expected outcomes. It's important that both you and your employee agree on these action items to ensure accountability and commitment. Outline how progress will be monitored and when you'll follow up, creating a roadmap for their development.

While it might seem like a lot of progress was made during a coaching session, for results to be long-lasting, employees need additional support afterward. In fact, consistency is crucial in maintaining the momentum created during your sessions. Schedule regular one-on-one meetings, whether weekly or biweekly, so that you can follow up on action items, address new challenges, and provide ongoing support. Regular check-ins reinforce accountability and allow you to adjust coaching approaches as necessary based on the employee's evolving needs.

By following these guidelines, you can facilitate effective one-on-one coaching sessions that not only enhance individual performance but also strengthen team dynamics and contribute positively to the workplace culture.

Following the steps will only get you so far, as you also need to keep in mind a set of best practices that will make success that much easier to achieve. Let's see what those are:

To ensure your coaching efforts are delivering results, consider tracking the following key metrics:

Combining qualitative insights with quantitative data provides a clear picture of impact, enabling you to make informed decisions and adjustments to your coaching strategies.

One-on-one coaching is a strategic approach that managers must learn to utilize so that they can maximize individual and organizational success. By shifting their focus from one-size-fits-all training programs to personalized support, managers can truly unlock their team members' full potential. When implemented correctly, one-on-one coaching can foster stronger workplace relationships, improve engagement, accelerate skill development, and help create a culture of continuous learning.

If you want to make the most of one-on-one coaching initiatives, it is essential to approach them with intention. Put your employees' needs and aspirations first, engage in active listening, ask meaningful questions, and focus on long-term results rather than short-term fixes. This way, you will eventually be able to help your employees become more confident, knowledgeable, and productive in their roles.
 
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A Gilded Lounge Singer's Many Day Jobs


You're reading a past edition of our weekly Things to Do column, about the concerts, art shows, comedy sets, movies, readings, and plays we're attending each week. Read the current installment. Sign up to receive it in your inbox.

Adapting your résumé into a book is not the most advisable art project. But then most of us haven't had Rex Marshall's life. If you've ever sat at Keys Lounge and... wondered how the man onstage in the gold lamé suit, the one singing into an Elvis microphone like an Oscar statue come to life -- if you've ever sat back and wondered how that dude, who sings under the nom de scène Mattress, got there, have I got a book for you. All the Work I Never Wanted is Marshall's account of shitty jobs, a slim and punchy "memoirella" from Portland small press Banana Pitch. The launch party is of course at Keys, Friday, May 1, at 7pm.

The book's chapters come like stories told in smoky green rooms and the backs of band vans. Marshall is a raconteur. But he's uninterested in packaging his CV into instructive fables. Stories of working at McDonald's (twice), at Michaels Arts and Crafts, and at the Convention Center Holiday Inn ("Hotels are really all about sex and it was the horniest job I've ever had") leave the reader to draw their own larger picture. Instead, Marshall's work tales achieve the texture of one job leading to another, aggregating to a statement about how little say we often have in how we spend our time.

The book's first and longest vignette tells of a newspaper hustle Marshall helped his dad with during high school summers. They lived in Vegas and worked overnight delivering the Las Vegas Review-Journal, which sounds like the most fantastically make-believe publication. His dad was cruel. Marshall bled into the newsprint most nights, loading 1,000 papers into the car, then folding, rubber-banding, and throwing them out the window -- hopefully before the sun came up. One night, another of the delivery guys fails to show. They curse him, having to pick up his route. He no-shows the next day. And again until, as they're delivering the Saturday paper, Marshall and his dad see a photo of the guy's exploded gold Toyota melted into the Blue Diamond Highway.
 
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Questions You'll Likely Hear in an Interview -- and How to Answer Them


Here's how to answer some of the most common job interview questions.

Editor's Note: This story originally appeared on Zety.com.

Job interviews are not for the faint of heart. As a candidate, you need to make an impression and showcase your accomplishments and skills -- all while making sure you find out as much as possible about your potential future job.

We've decided to compile a... comprehensive resource on interview questions, along with examples of how to answer them -- so that you know what to expect and come prepared.

This guide will show you:

* Common interview questions and answers that will get you the job you want.

* How to answer the most common interview questions better than 9 out of 10 other candidates.

* Examples of good interview questions to direct to the hiring manager to find out more about your future job.

Top Interview Questions to Expect

While it's hard to predict how creative the hiring manager will get, we still have an idea of the most common interview questions.

Well, if you go into an interview, you'll most likely be asked for:

* Some form of self-introduction

* Something about yourself (they usually mean professionally)

* What you consider your strengths and weaknesses

You may also be asked about how you handled (or would handle) certain situations, specifically those involving challenges or conflicts. Those kinds of questions are known as behavioral (or situational interview questions).

You should also expect questions about your previous jobs, career plans, and expectations.

Common Interview Questions With Answers

Here are some typical interview questions with brief explanations on why they are asked, plus sample answers that you can use as inspiration.

1. Where Did You Hear About This Position?

While this seems like a very simple question, your answer may come with extra benefits. The employer wants to know how you found out about them, and it's a great way to:

* Show that you know someone in the company if you got a referral (which means somebody who knows the company well thought you'd be a great fit).

* Share that you've learned about the company through research (you came prepared and know what you want).

* Express your genuine interest (maybe you've been keeping an eye on the company for a while, which means you're familiar with their operations/values/messaging, etc.)

Think along the lines of:

My goal was to work for a top marketing agency, so while I was doing my research via LinkedIn, I came across the job ad from OffBrand. I was immediately attracted by your one-of-a-kind tone of voice and amazing visuals. After looking at your portfolio and learning about your values, I knew I had to apply.

2. What Prompted You to Apply?

If you feel inclined to say, "I really needed cash," be sure to fight this urge. What the employer is looking for is:

* This position aligns with your career goals.

* Your previous experience makes perfect sense for this role.

* The company will clearly benefit from your knowledge and expertise, etc.

This is why it's important to do preliminary research into the company and ensure you've studied the job ad carefully. If you know what they're looking for, you'll know how to formulate your response:

This is an opportunity for me to "marry" my love for tech and my desire to create great products, ultimately. I've been working as a software developer for over 5 years before deciding to transition into the product owner role.

Due to my strong technical background, I know exactly how to steer the client's expectations toward what's actually doable. The benefits are twofold: the customer's needs are met without burdening the team with unmanageable tasks.

3. Do You Think You're Overqualified for This Position?

While the question might seem terrifying, this is a scenario both you and the recruiter would really like to avoid. According to recruitment statistics we've collected, a whopping 68% of professionals say they are overqualified for their current positions, which can mean that they are:

* Underpaid for the actual qualifications they possess

* In a position where they have no room for growth

* Lacking job satisfaction, and may end up feeling resentful and burnt-out

For the employing side, hiring someone who's not a good fit is expensive and unnecessary, as you'll be a potential "flight risk" if you stay. So make sure you ask questions (more on that later) to get as many details about the responsibilities as you can and be honest about what you think:

I appreciate you asking this. While I know I'm competent in editing, I have no managerial experience, which is my desired potential next step. This is why this assistant role makes a lot of sense: I can also learn a lot while doing it.

4. How Do You Handle Pressure/Stress?

The reason for this common interview question is quite obvious: many studies prove that stress can significantly decrease an employee's engagement, performance, and overall job satisfaction. If the job is deemed high-pressure (and let's be honest, many jobs today are like that), the recruiter will want to make sure you're able to manage that stress efficiently.

However, it's also the employer's responsibility to create reasonable working conditions, so you can (and should) definitely ask what could potentially contribute to the level of stress at this position:

I have experienced burnout before, which led me to reassess my time management and work-life balance approach. Now I'm confident that I can deal with stress well, noticing the early signs and tackling them before they have a chance to affect my work.

If I may ask, what would you consider to be the main potential stress factors for this role?

5. Are You More of a Team Player or an Independent Individual?

To answer this well, think of the expectations tied to the position. If you're applying to be head of the department but say you're "better off working on your own," it may be a red flag.

Still, don't pretend you're the "life of the party" when you'd much rather talk to your dog than other people.

I like working in sync with others -- I think the best ideas are born out of collaboration! However, I can be a self-starter, too. I'm able to work independently and develop a game plan for myself, executing it effectively and taking responsibility for its outcome.

6. What Is It That You Like About This Company?

Ah, the perfect way to find out whether you came prepared. It's not a coincidence that questions like "Why do you want to work here?" are an interview staple. Recruiters want to make sure you:

* Take this job seriously

* Did your research

* Actually like the company

* Know what you're signing up for

Studies have shown that unmet employee expectations result in decreased loyalty and productivity, leading to high turnover rates. Are we surprised? Definitely not. So you need to show that you did your part in figuring out what working for X would be all about:

Glad you asked! While doing my research and applying, I realized that I was impressed by how smoothly everything was going. Every detail was thought-through, and every step was a logical continuation of the previous one. And now that I've had the pleasure of talking to you and your colleagues, I've realized that this is how Chia-Valry actually operates.

For a position like mine that involves a lot of creativity, it's crucial to know that there are well-defined processes in place that you can rely on. Was my impression correct?

7. Would You Be Willing to Relocate?

Well, there's no secret to this one. If you think relocation might be in the cards for this job, you must thoroughly assess the pros and cons before making a decision.

Remember that you don't necessarily have to answer right away! It's a big commitment, so make sure you ask the hiring manager about how the company would compensate it, whether they're providing help with accommodations, etc.:

It's definitely something I would consider. I just need to know the details. Have you successfully relocated other employees? What was the process like?

8. What Was the Last Book You Read?

Probably one of the all-time pet peeves, this common interview question is supposed to give the hiring manager insight into your personality. It's probably not as popular as it used to be (the question, not your personality), but think of a book just in case. If it was "Fifty Shades of Gray" by any chance, maybe choose the one before that.

It would be nice if the piece had some connection to your job, but it's not a must. Make a very brief synopsis and say why you liked it:

I'm a huge fan of modern art, so the last book I read was "Breakfast at Sotheby's." It's penned by one of the high-ranking Sotheby's employees and reveals how art is evaluated, how the prices are formed, what contributes to the appeal, and if your child could actually have drawn it (smile).

A lot of what's in there can actually be translated into marketing, so I was surprised to find out that I could use the concepts from the book at work!

9. Did You Ever Have a Major Mishap at Work?

No one's perfect. But why would you want to throw yourself under the bus this way? Don't worry, this interview question is not aimed at finding faults with you. The recruiter wants to see how you handled the tough situation or what you've learned from the mistake:

I was just transferred to a new project that involved new responsibilities. I had a bit of impostor syndrome and was afraid I'd not be able to handle it well enough. Being scared to ask for help and prove my "lack of competence," I ended up being completely burnt out soon and having to delay an important presentation by almost a week.

Even though it didn't impact the completion of the project, it made me reconsider my whole approach to work. I learned to communicate, not let things reach boiling point, and request assistance when I really need it. It made me a better professional and a better colleague, and I'm glad I could learn from this situation.

10. Could You Explain the Gap in Your Employment History?

Employment gaps are nothing to be ashamed of. Whether you took time off to study, for medical reasons, or just because you needed a breather, all are OK -- just be honest about it.

There's no need to try to conceal them or invent creative cover-up stories. Just make sure you focus on the good outcomes -- it's about how well you can explain and justify those gaps:

Yes, I did have a gap of about 6 months in 2018. Several factors influenced that decision: I was severely burned out from the intense work tempo I'd been maintaining for 3 years prior, I had money saved up, and there was a trip I'd been postponing for years.

I took that trip, and it was a wonderful chance to realign my goals, re-think my career, and come back with new ideas and aspirations.

11. Are You Comfortable Working for Someone Who Knows Less Than You Do?

Ouch! One of those weird interview questions that definitely has a hidden agenda. How do you answer it elegantly, and what do they want to figure out about you?

Well, a couple of things:

* How you see yourself

* How you treat other people

So, what you shouldn't say is:

* "Hell no, I'm not coming to work for some inexperienced noob."

* "Why would my supervisor be inexperienced? Should I supervise them?"

What you could say is:

I believe that if someone is made a manager, they have the necessary skills for the job. I'd be glad to learn from them and share what I know.

12. What Was Your Previous Boss Like?

The first rule of Fight Club? Do not talk about Fight Club. The first rule of interviews? Do not talk poorly about your former employer, no matter what.

Someday, this job will become an "ex," too. And your potential colleagues would not like to learn that you'll talk behind their backs when you leave. Focus on the opportunities you received or what you've understood along the way:

A manager with a very clear vision and expectations. Even though my former boss and I didn't always see eye to eye, I am forever grateful for all the lessons I learned from him.

He was quite stern, but I learned to be organized, manage my time better, and take responsibility for my actions. Also, to notice red flags when taking on a new job and to speak my mind.

13. Why Were You Fired?

This is not the recruiter providing you space for a friendly rant. Be honest but polite. Remember that if you lie, it's possible for the hiring side to contact your old boss to verify. Keep the answer brief and to the point, explaining why it happened and what you did to make sure it never happens again (unless it was downsizing, etc.):

I was going through a trial period, and within the first month, I realized the job was not what I was looking for. This affected my motivation and productivity, but I thought I was not ready to go through another job search again.

Obviously, my results reflected what I felt, and I was fired when they decided not to extend my contract. While it was quite an unpleasant experience, I learned about what I really wanted in a job and that I don't have to settle.

14. Why Have You Changed Jobs so Frequently?

You must have had good reasons, so now you have to persuade the interviewer of the same. Maybe some personal events drove your decisions, maybe you chose to do freelance work, maybe something changed within the company, and you went to pursue better opportunities.

In any case, speak about the positive experiences and skills you've acquired along the way and re-aligning with your career goals:

It was never my intention to change jobs a lot. I loved my job at Eureka, but unfortunately, they had to relocate their office. I couldn't move for personal reasons, so I had to quit. I joined Restart then but soon realized that I was overqualified for that job, so together with my boss, we came to the decision that it was better if I moved on to something that matched my expertise.

15. What Are Your Salary Expectations?

If we're talking about expectations, this is definitely a question to expect at the end of your interview. It can certainly be daunting, so we highly recommend preparing in advance.

Because this is such a common interview question, we've made a full guide on it. Read more: How to Negotiate Salary Offer.

Best Questions to Ask the Hiring Manager

By the way, did you know you can (and should) ask questions during your job interview?

It's a great way to show your interest in the position and the company, your knowledge of the processes, and your desire to work at a place that is a good fit for you.

Take the time to think those questions through before you go into the interview to avoid losing that opportunity due to stress. Consider the topics that matter to you, are important for your future professional development, or are deal-breakers.

Good interview questions to ask at the end of an interview include:

* What is a typical workday/project/workload like?

* What are the main challenges for this position?

* What is onboarding like?

* Are there any learning opportunities/resources?

* If there are promotion/growth opportunities, what's a typical trajectory for this role?

* What are the performance expectations, and how are they measured/reviewed?

You can also ask those throughout the interview if it makes more sense, but recruiters usually specifically dedicate some time toward the end.

Key Takeaways

Here's how to give great answers to good interview questions coming your way:
 
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Impact Career Growth Introduces Enhanced 'Impact Accelerator' Program to Support Corporate Professionals Navigating Competitive Job Market


The Impact Accelerator program moves beyond conventional career coaching approaches that often emphasize résumé updates and general networking advice. Instead, it offers a structured, campaign-based strategy that helps professionals connect more directly with hiring decision-makers and executive search firms, bypassing the limitations of traditional online application processes.

"The reality is... that many qualified professionals are not being seen, despite strong experience," said Andrea Tropeano, Founder of Impact Career Growth. "This program is designed to help individuals communicate their value , and engage directly with the right stakeholders in a more intentional and strategic way."

(In Frame: Andrea Tropeano, Founder of Impact Career Growth)

To view an enhanced version of this graphic, please visit:

https://images.newsfilecorp.com/files/12386/295977_8dcae21c945839a4_00 ...

Delivered virtually to clients across the United States, the program has a 5-step framework - Focus, Prepare, Activate, Execute, and Land. The fast-track program combines resume development, LinkedIn optimization, interview preparation, and effective targeted outreach to improve response rates from decision makers.

Tropeano brings vital and relevant perspectives to her work, drawing on experience in corporate recruiting, human resources, executive search, and career coaching. Having reviewed thousands of applications and collaborated closely with hiring managers, she offers insight into how candidates are evaluated, how applicant tracking systems filter profiles, and how shortlists are constructed.

"Understanding how hiring decisions are actually made allows professionals to approach their search more strategically," she noted. "It's not just about visibility, it's about relevance and timing."

In addition to the Impact Accelerator, the firm offers a career rediscovery program for professionals considering a career pivot or evaluating whether to stay in their current roles. The program includes a series of assessments and coaching sessions designed to help individuals gain clarity around their strengths, motivations, direction, and long-term career alignment.
 
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Early-career employees explore what's N.E.X.T. in Aviation careers


To help early-career employees define their professional paths and expand their understanding of career options within Defense Logistics Agency Aviation, the Procurement Process Support Directorate, often referred to as BP, hosted the "What's N.E.X.T.?" career development briefing at Defense Supply Center Richmond.

N.E.X.T., which stands for Navigate, Excel, Xpand, Thrive, is a new initiative... designed to equip recent graduates of the Pathways to Career Excellence (PaCE) Program with tools and insights to shape their careers within the acquisition workforce.

The session brought together employees, hiring managers and subject matter experts to focus on three areas: self-awareness, team-building skills, and career path exploration. Participants identified personal strengths, refined their goals, and learned about varied roles available within their occupational series.

The morning began with an icebreaker activity aimed at networking among contract specialists and sparking conversations about individual career roadmaps. Sherry Mitchell, branch chief within Procurement Training Division, and Jennifer Deskins, senior analyst within Procurement Process Support, opened the session with presentations on aligning career plans with personality, interests, and values.

The team also created a personalized career development workbook for participants, encouraging ongoing self-reflection and skills tracking.

"The icebreaker game at the beginning was a great way to connect and hear about different career opportunities in an informal manner," said Julie Best, contract specialist within Strategic Acquisitions Division. "The slides and the information shared during the presentation were beneficial. I appreciate opportunities such as the N.E.X.T. Career Development Briefing as it provides action items if you are willing to take the information and utilize it. Being part of the PaCE program has provided me with so many opportunities for career development. There are so many wonderful people part of the program, and I know that is what drives the program's success. Throughout my entire time in the program, there was intentionality in the planning and a true respect and appreciation for feedback."

Gwendolyn Pearson and Alicia Jones, program managers within Procurement Process Support, spoke about certification and warrant requirements, providing insight into career progression within the acquisition field.

"My hope for career development is to see what options are presented, and that it doesn't restrict us to follow that one path, and this event was able to show just that," said Arjun Mandgi, contract administrator within OEM Post-Award Division.

Another key portion of the sessionincluded briefings from experienced hiring managers Rick Alexander, division chief of Strategic Acquisitions, and Floyd Moore, director of the Engineering Directorate. Both emphasized transparency in hiring practices and shared strategies for lateral and upward mobility.

"Sharing perspective with PaCErs as they enter Federal Service is an investment in the future workforce.Mentoring begins now as we prepare them to take our places someday," said Alexander. "Helping them get off on the right foot with realistic expectations, targeted career goals, and a commitment to expertise development is mutually beneficial to each individual and to the organization."

"Throughout my career, many people have taken time to mentor and develop me.Nothing good I have achieved has happened alone. I stand on the shoulders of greatness," he added. "This was an opportunity to pay it forward. It is a delight to share the journey with passionate and committed public servants at every stage of their career as we pull together to accomplish the mission."

Moore said he's "instilled with a sense of obligation to support and mentor colleagues," and "strives to fulfill that commitment daily."

The N.E.X.T. briefing is one of several efforts to build a culture of growth and professional development among early-career employees at DLA Aviation.
 
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Will A.I. Make College Obsolete?


Jay Caspian Kang writes that more and more families may decided that college isn't worth the cost, amid the rise of A.I. and easily found information.

For the next few weeks of this column, I will dig into questions about the viability of the American university system. The pressures on higher education seem extraordinary, even to someone like me, who is generally convinced that real change is... rare, perhaps especially when it comes to America's tried-and-tested system for replicating its élites.

Private and state universities have had their funding cut by the Trump Administration, professors report rampant A.I. -assisted cheating by their students, and seemingly every week brings a new report about how nearly all entry-level white-collar jobs -- whether they're in consulting, insurance, finance, management, or the sciences -- will be replaced by friendly chatbots that may or may not someday destroy the world.

A recent survey found that more than one in four college students in America believe that their tuition was not a good investment, at a time when more than forty per cent of college graduates between the ages of twenty-two and twenty-seven hold a job that does not require a college degree. According to Pew, seven in ten Americans think that the "higher education system in the United States is going in the wrong direction," with most respondents expressing concern about the high price of tuition.

With all this happening, should I continue to contribute to my children's 529s? The short, easy, and most likely correct answer is yes -- I should assume that, when my nine-year-old reaches high school, she will go through the familiar gantlet of academic competition and spend much of her time building a résumé for college-admissions committees. I should also assume that the cost of whatever college she attends will not come down during the next nine years.

The university system in America has survived worse than A.I. : pandemics, wars, campus unrest, massive open online courses, the internet. If colleges seem impervious to revolutions in information technology, is it possibly because their actual appeal has less to do with the transfer of knowledge than their administrators might want to admit? As the economist Bryan Caplan has observed, "The main function of education is not to teach useful skills , but to certify students' employability.

By and large, the reason our customers are on campus is to credibly show, or 'signal,' their intelligence, work ethic, and sheer conformity. " As long as college remains a way for upwardly mobile kids to stand out from one another, and as long as employers believe that a better college degree is a sign of a better potential worker, the American university system should survive, even if teaching methods change.

Nonetheless, it seems a bit odd that, when it comes to predictions about our A.I. future, which typically range from friendly revolution to organ-harvesting apocalypse, declarations about higher education have been relatively mellow. Granted, many of the commentators offering these predictions are employed by traditional universities, and might tend to believe more strongly in the enduring relevance of the academy. There are exceptions: the OpenAI C.E.

O. Sam Altman has suggested that his own kid might not attend college; Howard Gardner, a psychology professor at Harvard, recently surmised that A.I. will significantly shorten the time children need to be in school. But the consensus is that college will still exist in ten or twenty or thirty years, a forecast that, for a parent of two staring down future tuition bills, is a bit disappointing.

Even some pundits who are open to A.I. as a major development agree that higher education isn't going anywhere. Tyler Cowen, for instance, Caplan's colleague in George Mason University's economics department, has argued that more instruction time should be devoted to A.I. in American classrooms -- and mused that A.I. might help students better understand the Odyssey -- but maintains that the traditional subjects and pedagogy of higher education should largely remain intact.

Sal Khan, the founder of the free online-learning service Khan Academy, has launched a partnership with TED and the Educational Testing Service called the Khan TED Institute, which aims to provide a "world-class higher education accessible throughout the world at a radically low cost. " But Khan doesn't see his latest venture as a wholesale replacement for the brick-and-mortar university; he has described it as a reasonably priced alternative that can keep pace with a world that is changing "very, very fast.

" Scott Galloway, a professor, a popular podcaster, and perhaps the most influential public voice on the value of a university education, has declared that "this narrative that A.I. is going to destroy higher education is such ridiculous bullshit.

" Higher education could drastically change soon, he says, if tech giants start partnering with prestigious universities to expand their enrollment through online degrees, thereby effectively shutting down hundreds of smaller, private colleges. But those changes would be driven by supply and demand, rather than a fundamental shift in opinion about whether it's still good to go somewhere, in person, to learn things.

I don't believe that these thinkers are necessarily wrong to dismiss the idea that enormous changes will come to higher education during the next two decades; as long as Americans want to distinguish their children from other children, the hierarchical college system will prevail. But these defenses of higher education feel almost performatively cynical, especially for an institution that has traditionally draped itself in high-flown sentiment about the pursuit of truth and the shaping of young minds, or whatever.

I also wonder if the skeptics might be overstating the power of inertia, especially at a time of extremely low public trust in all institutions, not just those of higher education.

In the world of prestige media that includes The New Yorker, for example, it has long been much harder to break in without an Ivy League degree, and that remains the case; but the draw of working at a legacy-media institution has also never been weaker. Would a fifteen-year-old hellbent on a journalism career be best served by working himself to the bone both academically and extracurricularly to get into Harvard, or should he just start a Twitch stream and get to work?

Reasonable people can disagree about that. But I feel certain that most of the ambitious fifteen-year-olds who already know what they want to do these days would choose the self-made option -- particularly if they come from families that can't easily afford college tuition, let alone thousands of dollars in supplemental application prep.

A.I. might not factor directly into such a decision for an aspiring reporter, but the already impressive abilities of large language models to hone research, approximate historical knowledge, and target potential sources would soften any disadvantages that this hypothetical student might suffer from skipping college. Perhaps this ambitious teen would be more susceptible to the algorithmic and predictive gutters of these machines -- when the A.I. companies set the guidelines for what the L.L.

M. says back, you will always be receiving their version of the truth -- but professors and college curricula also have their gutters, some of which are far deeper than what you'll find at the bottom of Claude. Can college really be laid so bare and survive?

Will the roughly sixty per cent of recent high-school graduates who invest in higher education still see the value of it if they come to believe -- rightly or wrongly -- that the whole knowledge part of college has been replaced by an agreeable chatbot? Our hypothetical ambitious fifteen-year-old is exceptional, of course, and certainly not the bellwether for today's disaffection about higher education.

Few teen-agers know what they want to do in life, and it's not always good for kids of that age to limit their choices. What I find concerning, however, is that so many other white-collar industries and professions -- finance, consulting, the law -- are even more institutional in their thinking than the media is.

They, too, are held in low esteem by the public, and that decline in trust has frayed the traditional line of thinking that you should join one storied institution, a university, to later work at another. If we agree that college primarily serves a credentialling process that stamps select young people as worthy of work, and, if we agree that A.I. helps to expose it as such, might we not conclude that, at some point, people will collectively stop paying into the system, or will start seeking out other, less expensive credentials?

I do not think that A.I. will singlehandedly destroy college. But I do think that it will accelerate an already growing disillusionment with higher education. In 2013, seventy-four per cent of eighteen-to-thirty-four-year-olds polled by Gallup said that a college education was "very important.

" By 2019, three years before the public adoption of ChatGPT, that number had dropped to forty-three per cent; it fell again, in 2025, to thirty-five per cent, a decline that represented the steepest drop among all age groups that were surveyed. This drop might level off at some point, simply because most things regress to previous norms.

But I cannot come up with any reason why the trend would reverse direction without radical changes to cost and access at the types of élite colleges that facilitate class mobility. What seems likely is a winner-takes-all scenario, in which the élite schools and flagship state universities survive on account of their cultural, financial, and reputational advantages, while other schools die out, leading to either a huge expansion in enrollment among the survivors or a steady drop in the number of young people who seek out a four-year degree.

That may be a good outcome, but the gospel that I grew up with -- the idea that everyone should get a college education not only for upward mobility but also to explore reading, thinking, and writing for their own sake -- will be dead. The future of college as we know it may rely on the ability of people who have a stake in the credentialling economy to convince the youth that there is still value in classroom instruction, in writing papers without A.I. assistance, in talking to imperfect humans about misshapen ideas.

But they will be making this case to a generation of students who learned many things -- skateboarding, the piano, cooking -- from YouTube, and who have been able to ask Claude to assist them in every academic endeavor they've undertaken. Who will be the most receptive audience for this sales pitch? Probably those who trust institutions the most, and who can sacrifice some efficiency for an outdated but fancy stamp of approval -- in other words, the children of the wealthy and educated.

But, when you consider that the vast majority of students at élite private colleges -- which is to say, this same group -- already use A.I. in nearly all aspects of their academic lives, it can seem as though this fight has already been lost. College will still exist as a place -- or, at least, as a website or app -- that employers will use to distinguish one applicant from another.

But will it still look the way it does today, with thousands of campuses around the country, of varying reputation, quality, financial health, and philosophical missions? We'll get into all that next week. ♦

Higher Education College

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Don't chose 11 am slot for an interview, says a career consultant. Google Gemini mostly agrees and gives the reason


Job interview anxiety is common. Career coach Simon Ingari suggests avoiding 11 am interview slots. He believes interviewers may be tired or distracted then. Some internet users agree, preferring mid-afternoon slots. However, other users and AI models like ChatGPT disagree. They argue 11 am can be a peak productivity time for interviewers.

Job interview anxiety is something almost every... professional experiences, whether it's preparing for tough questions, waiting nervously for your turn, or managing stress during the actual conversation. From sleepless nights and overthinking resumes to second-guessing answers and fearing rejection, interview stress can feel overwhelming. The pressure to impress recruiters while staying confident often makes the process mentally exhausting. But do you know that n today's competitive job market, even choosing the right interview time can influence performance? Career coach Simon Ingari echoes a similar sentiment in one of his latest X-posts.

In the post, which received over 14 million views, Simon Ingari made a rather bold claim, stating that when an HR representative of a company asks a candidate to choose an interview time, job-seekers must avoid the 11 am slot. His surprising advice has received several comments, forcing many to rethink their interview strategy.

One user speculated that by 11 am, HR professionals are often already drained from back-to-back morning meetings, overflowing inboxes, and workplace demands. With lunchtime approaching, they may struggle to focus entirely on a candidate, potentially affecting the interview dynamic. Suggesting that mid-afternoon may be a better choice, the user recommended opting for a 3 pm slot instead, when interviewers could be more settled and attentive.

'11:00 AM is dangerous. You are basically being judged by someone whose coffee has worn off and lunch has not arrived,' agreed another. Unable to understand Simon Ingari's advice, an individual asked, 'Why avoid 11:00? something about timing or energy levels around then?' Begging to differ, someone else noted that if a candidate is confident and well-prepared, they have the potential to crack an interview at anytime of the day, be it 11 am or 11 pm.

The hunger factor: By 11:00 AM, interviewers may already feel mental fatigue after hours of decision-making, while hunger and approaching lunch can reduce patience, focus, and mood -- potentially making them more rushed or irritable during your interview.

Mid-morning pileup: Around 11:00 AM, unfinished meetings, urgent tasks, or workplace issues often build up, leaving interviewers distracted or pressured by pending responsibilities instead of being fully engaged in evaluating you.

Serial position effect: Psychologically, candidates interviewed first or last are often remembered best. The 11:00 AM slot can fall into the less memorable middle, where you risk being overshadowed and associated with pre-lunch distraction.

Peak productivity window: By 11:00 AM, many interviewers are fully alert, settled into their workday, and past early-morning distractions, allowing for sharper focus, better engagement, and more thoughtful evaluation than rushed first-slot interviews.

Avoiding early chaos: Unlike early morning slots, 11:00 AM often comes after inbox checks, team updates, or urgent priorities are handled, meaning interviewers may be more present and less distracted during your conversation.

Balanced timing advantage: Positioned before lunch but after the day stabilizes, 11:00 AM can offer a practical middle ground, avoiding both morning grogginess and late-day fatigue while giving candidates enough preparation time.
 
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  • is more

  • Get into building online businesses instead of over the phone. Build a website for it and people are far more likely to spend far more time on it... than conversation, because a phone or computer is not only theirs, but is faster than interfacing with the real world and they can go at their own pace. more

Top Employee Engagement Strategies for 2026


Have you ever watched your team drift through the workday, going through the motions but not really present? People scroll past emails without reading them. Meetings feel like a waste of time. Productivity drops, morale sinks, and you can tell something is off, but you can't quite pinpoint what needs to change. Here's the good news: you can fix this.

Recent research shows that companies with... strong employee engagement strategies see 21 percent higher profitability than their competitors. That's a massive difference. Engaged workers stay longer, produce better work, and genuinely care about where the company is headed.

They become your best advocates, telling friends and colleagues how great your workplace is. This post walks you through the most effective employee engagement strategies for 2026. You'll find practical steps to boost morale, sharpen communication, and build a place where people actually want to show up.

What Is Employee Engagement?

Employee engagement means your team members care about their work and your company's success. Engaged employees show up with energy, put in real effort, and stick around longer. They feel connected to their jobs, their coworkers, and the organization's mission. This connection goes beyond collecting a paycheck. It's about finding meaning in what they do each day.

According to February 2026 polling data from Gallup, just 31% of U.S. full-time employees are actively engaged at work. That's an 11-year low, down from 36% in 2020. It means that nearly 70% of your workforce may be disengaged or quietly quitting right now.

That's a serious problem, but it also shows just how much room there is to improve. Here's what an engaged employee actually looks like in practice:

* They bring energy and focus to their work each day

* They care about team goals, not just personal tasks

* They stay longer and contribute more to organizational culture

* They speak positively about your company to people outside it

Think of engagement as the spark that ignites workforce motivation and job satisfaction. Your staff retention rates climb when people feel valued and heard. Engaged employees collaborate better, communicate more openly, and deliver stronger results across the board.

Why Is Employee Engagement Important?

Engaged workers drive real results. They show up ready to contribute, they care about quality, and they stick around longer than disengaged staff. When your team feels connected to their work and valued by leadership, productivity soars. Staff retention improves because people don't leave jobs they actually enjoy.

Morale lifts across departments, and collaboration happens naturally. Motivation flows from genuine investment in the work itself, not just from paychecks. Companies that prioritize employee engagement see their bottom line improve because focused workers accomplish more in less time.

Disengagement costs money, plain and simple. A March 2026 analysis by Paycor reveals that low employee engagement costs U.S. companies approximately $2 trillion in lost productivity every year. That's not a soft HR metric. It's a massive financial hit that touches every level of the business.

When talented people walk out the door, turnover expenses pile up fast. Lost productivity hits hard when workers go through the motions without real commitment. Poor communication and lack of recognition create frustration that spreads quickly through teams. Career development stalls, and your best performers start looking elsewhere for growth. Performance management becomes reactive instead of proactive. Job satisfaction drops, absenteeism rises, and quality dips.

* Recruiting and training new staff drains time and money

* Disengaged workers drag down team morale

* Poor recognition leads to high turnover

* Lack of feedback blocks career growth

* Leadership gaps multiply performance problems

Organizations that ignore employee engagement spend all their energy recruiting, training, and rebuilding their workforce. The cost of replacing a single employee often reaches thousands of dollars. Engagement strategies are simply smart business.

Key Benefits of Employee Engagement

When you invest in employee engagement, your organization gains stronger productivity, holds onto talented staff longer, watches employee mental health improve, and sees profits climb.

Increased productivity

Engaged employees work harder, faster, and smarter. They bring real energy to their jobs, which lifts workplace productivity to new heights. Staff retention improves when workers feel valued. This stability creates momentum. Your team gets more done in less time because people genuinely care about their work.

Workplace productivity soars when leaders foster team collaboration and open communication strategies. Employees who receive regular feedback know exactly what success looks like, so they hit targets with confidence.

"Engaged employees produce better results because they care about what they do."

Career development opportunities keep people sharp. Job satisfaction rises when workers have autonomy over how they complete tasks. Performance management systems that feel fair encourage people to bring their best selves to work every day. Your bottom line benefits when you prioritize employee satisfaction and empowerment across the organization.

Higher employee retention

Productivity gains mean nothing if your best people walk out the door. Staff retention directly ties to your bottom line, since replacing workers costs money and time. Companies that invest in employee satisfaction see their teams stick around longer. Strong job satisfaction keeps talented staff from jumping ship to competitors. Your workforce motivation skyrockets when people feel valued and heard at work.

Career development opportunities lock in your top performers for years to come. Employees who see clear paths forward stay put, build greater skills, and contribute more to organizational culture.

* Recognition practices show staff that leadership values their growth

* Mentorship programs build deeper connections and skills

* High retention cuts hiring costs significantly

* Long-tenured employees carry institutional knowledge that money can't replace

Improved mental wellness

Keeping your team happy at work directly impacts their mental health and job satisfaction. Employees who feel valued and supported show up with better morale and less stress. Strong retention rates mean workers stay longer, build deeper relationships with colleagues, and develop a real sense of belonging. This stability creates a foundation where mental wellness can actually flourish.

Your staff experience lower anxiety when they know their jobs are secure and their contributions matter. Mental health struggles drop when people feel heard through open communication and honest feedback. Career development opportunities give workers a sense of purpose and control over their futures. By supporting mental wellness, you build a workplace culture where people genuinely want to show up and perform at their best.

Enhanced organizational profitability

Engaged employees drive your bottom line straight up. When staff members feel valued, they produce more in less time. They make fewer mistakes. They stay longer. All of this cuts costs and boosts profits. Staff retention alone saves money on hiring and training. Productivity gains mean you get more output from your current workforce.

Here's what that looks like in practice:

* Fewer costly hiring cycles each year

* Higher output without adding headcount

* Stronger financial results from motivated teams

* Less rework and fewer quality errors

Companies with high employee satisfaction report stronger financial results year after year. Profitability grows when you invest in your people's career development and wellbeing.

Recognition and appreciation programs cost little but deliver big returns. Workforce motivation directly impacts your ability to compete in tough markets. Your organizational culture becomes a real asset when people want to show up and do their best work every single day.

Common Challenges in Employee Engagement Strategies

Even the best employee engagement strategies fail when leaders don't back them up or when teams can't communicate clearly. Companies often stumble because they don't recognize hard work, or they design jobs that leave people feeling stuck and powerless.

Lack of leadership support

Leadership support makes or breaks employee engagement efforts. Without buy-in from top management, engagement strategies crumble fast. Leaders who fail to champion workplace motivation send a clear message to staff: this doesn't matter. Employees pick up on that signal fast. They notice when executives skip town halls, ignore feedback, or refuse to invest in career development programs.

Weak leadership creates a culture where morale tanks, retention suffers, and organizational health deteriorates. According to the 2026 Predictions Report by Perceptyx, poor management drives turnover risk up by four times and costs U.S. businesses approximately $408 billion annually. That number is impossible to ignore.

Managers hold the keys to workforce motivation and job satisfaction. They shape daily interactions, model company values, and decide whether to support team collaboration or shut it down.

When leaders don't actively promote employee recognition or champion work design improvements, engagement scores plummet. Staff feel invisible and undervalued. The ripple effect spreads across departments.

* Performance management becomes inconsistent across teams

* Talent development stalls across the organization

* Workers stop believing their voices count

* Teams lose confidence in their direction

Leaders must show up, listen hard, and back their words with action and real resources.

Ineffective communication

Broken communication lines kill employee engagement faster than almost anything else. Managers fail to share company goals clearly, so workers feel lost and disconnected from the bigger picture.

Teams don't understand what leadership expects, feedback gets lost in translation, and rumors fill the gaps left by silence. This lack of transparency creates confusion, breeds frustration, and tanks morale across the entire workforce. Employees start to doubt whether their work matters, and motivation takes a nosedive.

Poor communication strategies also block career development and job satisfaction. Staff members don't know about internal mobility opportunities because announcements never reach them. Recognition gets buried in email chains nobody reads.

* Performance management becomes a guessing game without clear expectations

* Collaboration breaks down when roles feel disconnected

* Disengagement spreads when silence replaces transparency

* Top talent leaves for workplaces that communicate openly

The result: employees disengage, productivity drops, and your best talent walks out the door looking for a workplace that actually talks to them.

Poor recognition practices

Poor recognition practices create a massive gap between what employees do and what leaders acknowledge. When staff members work hard but hear nothing back, their motivation takes a nosedive. Managers often skip praise because they assume good work speaks for itself, or they simply forget to say thank you. This silence damages morale fast. Employees start feeling invisible, undervalued, and disconnected from their jobs.

Your best talent walks out the door looking for appreciation elsewhere. Recognition programs that lack authenticity or consistency fail even harder. Staff members see through fake praise or rewards that feel random and meaningless.

Strong recognition practices flip this script entirely. Leaders who celebrate wins, big and small, build workforce motivation that sticks. Personalization matters here. A sincere note about someone's specific contribution hits very differently than a generic email to the whole team.

* Tie recognition to specific behaviors and results

* Mix public praise with private acknowledgment based on individual preferences

* Make recognition consistent, not just a once-a-year event

* Connect appreciation to career development opportunities where possible

Your organizational culture transforms when recognition becomes part of how your team operates, not an afterthought tacked on at the annual review.

The Most Effective Employee Engagement Strategies for 2026

Companies that want their teams to thrive in 2026 need smart, practical strategies that actually deliver results. Here's what works.

Start with well-designed surveys

Well-designed surveys form the foundation of any strong employee engagement strategy. Your team members hold the answers you need, and surveys give them a voice to share their thoughts, concerns, and ideas. Ask specific questions about job satisfaction, workplace productivity, team collaboration, and organizational culture. Keep surveys short and straightforward so employees actually complete them without frustration.

The data you collect becomes your roadmap for improvement, showing you exactly where morale stands and what changes matter most to your workforce motivation. Act on what your surveys reveal. Employees notice when leaders ignore feedback, and that kills engagement fast. Share the results with your team, explain what you learned, and outline concrete steps you'll take based on their input.

This transparency builds trust and shows your staff that their voices shape real decisions. Different departments may need different support, so dig into the data by team.

Performance management improves when you ground it in actual employee perspectives rather than assumptions. Surveys become powerful tools for career development conversations and recognition practices when you use them thoughtfully.

Foster workplace autonomy

Giving your team members control over their work creates powerful motivation. Autonomy means employees make decisions about how they complete tasks, which projects they tackle, and when they work best.

This approach builds job satisfaction because people feel trusted and valued. Your staff takes ownership of their results when you step back from micromanaging. Productivity rises naturally when workers shape their own workflow.

Managers who grant workplace autonomy see real shifts in workforce motivation and performance. Your team members develop stronger skills because they solve problems independently. They feel more connected to their work when they have real control.

This freedom reduces stress and supports a better work-life balance. Staff retention improves because people stay longer at companies where they feel genuinely empowered.

Establish mentorship programs

Mentorship programs light a fire under workforce motivation by pairing experienced employees with newer team members. Senior staff share knowledge, offer guidance, and help junior employees navigate career paths. This type of talent development builds stronger connections across your organization. Employees feel valued when someone invests time in their growth. Mentors gain leadership experience and fresh perspectives from their mentees.

Both parties develop skills that boost workplace productivity and job satisfaction. Your team members see clear pathways for advancement, which keeps morale high and reduces turnover.

* Match mentors and mentees based on career goals and skills

* Schedule regular check-ins to track progress and address challenges early

* Let mentees gain hands-on experience through real projects

* Recognize mentors publicly for investing in others' growth

Employees who participate in mentorship programs report higher engagement levels and greater job satisfaction. Your organization builds a pipeline of prepared leaders ready to take on bigger roles. These relationships transform how people view their future with your company.

Encourage employee recognition and appreciation

Mentorship programs plant seeds for growth, yet recognition waters those seeds and helps them flourish. Employees thrive when their hard work gets noticed and celebrated. Recognition doesn't have to be fancy or expensive. A simple thank you, a shout-out in a team meeting, or a small bonus can lift morale sky-high. Staff retention improves when workers feel valued, and job satisfaction climbs when people know their efforts matter.

Appreciation comes in many forms, and personalization makes it stick. Based on Snappy's 2026 Workforce Study of U.S. employees, while 70% of companies have recently increased their recognition efforts, 73% of employees say recognition only feels genuine when it is highly personalized. Generic praise misses the mark. Specific, thoughtful acknowledgment is what actually moves people.

Some team members love public praise, while others prefer private acknowledgment. AI tools now help managers track accomplishments and suggest timely recognition moments, which takes the guesswork out of performance management.

When you tie recognition to specific behaviors and results, employees understand exactly what drives success in your organization. This approach builds stronger team collaboration, boosts workplace productivity, and creates a culture where people genuinely want to show up and do their best work.

Recognition programs that connect to career development opportunities make the impact even stronger, turning appreciation into real pathways for growth.

Promote internal mobility opportunities

Your staff members want to grow, and internal mobility gives them that chance. When employees see career paths within your organization, they stay longer and work harder.

According to 2026 workplace retention data from LinkedIn, employees at organizations with strong internal mobility stay an average of 5.4 years, compared to just 2.9 years at companies without it. That's an extra 2.5 years of experience, institutional knowledge, and productivity you keep in-house.

Companies that support staff retention through internal promotions cut turnover costs significantly. Let workers move between departments, take on new roles, and build fresh skills. This approach strengthens your organizational culture while filling positions with people who already know your company.

* Map out which roles connect to each other across departments

* Share internal opportunities openly through your communication channels

* Highlight the success stories of staff who advanced internally

* Give employees time to explore new skills before committing to a role change

When people see their colleagues climb the ladder, they believe advancement is possible for them too. That belief is powerful for workforce motivation and long-term job satisfaction.

Prioritize employee well-being and work-life balance

Your workforce thrives when they have time to recharge outside the office. Employees who maintain a healthy work-life balance show stronger job satisfaction and deliver better workplace productivity.

Companies that support wellbeing initiatives see staff retention rates climb significantly. Offering flexible schedules, remote work options, and reasonable workload expectations sends a clear message: your team members matter as people, not just workers.

Here are some well-being initiatives that make a real difference:

* Mental health resources and access to counseling support

* Wellness days and fitness program stipends

* Flexible start and finish times, where the role allows

* Regular workload check-ins to prevent burnout

Leadership development starts with leaders who model a good balance themselves. Showing that stepping away from work is not laziness, but a necessity, sends a powerful signal to your team.

Burnout kills motivation faster than almost anything else. Addressing workload and stress becomes critical for performance management. Collaboration improves when people feel rested and valued. Praise rings hollow if workers are exhausted. Organizations that invest in workforce motivation through wellness programs see teams communicate more openly and work together more effectively. This approach creates loyalty that lasts.

Emphasize transparent communication

Transparent communication forms the backbone of strong organizational health. Leaders who share information openly build trust with their teams. Employees feel valued when they know what's happening, why decisions get made, and how their work matters. This openness cuts through workplace confusion and reduces the rumor mill that spreads fast in offices.

Teams collaborate better when everyone has the same facts. Managers should communicate strategy, changes, and goals in plain language. Avoid corporate jargon that leaves people scratching their heads. When staff understand the big picture, they stay motivated and engaged.

* Town halls keep everyone aligned on the company's direction

* One-on-one meetings build trust and surface concerns early

* Anonymous suggestion tools give quieter voices a channel

* Regular team updates prevent rumors and fill information gaps

Feedback flows both ways in transparent cultures. Your team members need to hear from you, but you also need to hear from them. Create channels where employees can speak up without fear. Staff retention improves when people feel heard and respected. Your workforce motivation soars when transparency replaces secrecy. People perform better when they trust their leadership and know exactly where they stand.

Offer continuous learning and growth opportunities

Your team grows when you invest in their skills. Companies that offer continuous learning programs see higher job satisfaction and stronger staff retention rates.

Employees want career development paths, not dead-end jobs. Provide access to online courses, workshops, and certification programs that match your workers' goals. Let your people take time during work hours to learn new skills. This shows them you value their growth, and they repay that investment with better performance and loyalty.

Growth opportunities come in many forms, so mix them up:

* Pair junior staff with experienced mentors for talent development

* Rotate employees into different roles for broader experience

* Host lunch-and-learn sessions where team members share knowledge

* Offer tuition reimbursement for degrees or professional certifications

When employees see a clear path forward, their motivation climbs, and workplace productivity soars. These strategies build a culture where people feel empowered to reach higher. That energy spreads across your entire workforce.

How to Measure the Success of Employee Engagement Strategies

You'll want to track how your employee engagement strategies perform so you can spot what works and what needs fixing.

Use employee feedback surveys

Employee feedback surveys act as your organization's listening device. They give workers a real voice in how things run, and they show staff that leadership actually cares about their thoughts. Surveys reveal what drives your team, what frustrates them, and where they see growth opportunities. Companies that deploy regular feedback surveys see higher job satisfaction scores and stronger organizational health.

Make your surveys short and simple so employees actually complete them. Ask clear questions about work design, team collaboration, and career development opportunities. Track the feedback over time to measure real shifts in morale and motivation.

Act on what you learn, then tell your team what changes you made based on their input. This cycle of listening, acting, and reporting back builds trust. It shows workers that their voice shapes the workplace.

Track retention and turnover rates

Tracking retention and turnover rates gives you a clear picture of your staff retention strategy's real impact. Your turnover rate tells you what percentage of workers leave your company each year, while retention metrics show you who stays.

Pull these numbers every quarter to spot trends early. If your retention drops, you know something needs to change fast. High turnover costs money through recruitment, training, and lost productivity. Low turnover means your workplace productivity stays strong and your organizational culture remains stable.

Your data reveals which departments struggle most with keeping talent. Compare your turnover rates against industry standards to see if you're ahead or behind. Track which employees leave after six months versus those who stick around for years.

This performance management insight helps you fix problems in your onboarding or work design. When retention improves, your job satisfaction scores typically climb, too. Your team collaboration strengthens as people build longer relationships with coworkers.

Assess productivity metrics

Productivity metrics tell you what your workforce actually accomplishes. You can measure output per employee, project completion rates, sales figures, or customer service response times.

These numbers reveal whether your engagement strategies move the needle on real business results. Pull data from your project management tools, sales systems, and performance tracking software. Compare these metrics before and after you launch new engagement initiatives.

Your team's performance management system should track individual and team contributions. Look at the quality of work, not just the quantity. An employee might complete ten tasks, but did they complete them well?

Higher productivity often signals that your workforce motivation is climbing and your staff retention efforts are working. These metrics connect directly to organizational health and your bottom line, making them essential for measuring whether your employee engagement strategies deliver real value.

Final Thoughts

You now have a clear picture of the core strategies that drive employee engagement in 2026, from well-designed surveys to transparent communication that builds real trust across your organization. These approaches work because they center on what employees actually want: recognition, growth opportunities, and genuine work-life balance that respects their personal lives.

Start small. Pick one or two strategies that fit your company culture, then measure results through retention rates and feedback surveys to see what sticks. Your team members perform better when they feel valued, heard, and supported in their career development.

Ask yourself right now: which strategy could you launch this month to show your workforce you care about their wellbeing and job satisfaction? Your people are your greatest asset. When you fuel their motivation through collaboration and empowerment, your entire business transforms.

Frequently Asked Questions (FAQs) on Employee Engagement Strategies

1. What are the most effective employee engagement strategies for 2026?

Focus on open communication, flexible work options, and regular feedback. A 2025 Gallup study found that companies with flexible work arrangements report 21% higher engagement scores.

2. How can managers boost employee motivation in 2026?

Set clear goals and recognize good work often. When you celebrate small wins, motivation stays high.

3. Why does employee engagement matter for business success?

Engaged workers are more productive, stay longer, and contribute better ideas. According to a 2024 Gallup report, highly engaged teams show 23% higher profitability.

4. How can companies measure employee engagement levels?

Run short surveys, track turnover rates, and hold regular one-on-one check-ins. Tools like Culture Amp help you measure engagement quickly. When you act on what you learn, trust grows fast.
 
more

He Couldn't Land a Job Interview. Was AI to Blame?


Even recruiters will admit it's fair to wonder. The CEO of a hiring platform said last fall that his industry is in "an AI doom loop": HR departments complain of a wave of AI-generated job applications, prompting the need for more AI filters. Applicants complain they're getting unfairly filtered out. Some fight AI with AI, filling their résumés and cover letters with buzzwords. "It feels very... dystopian to me," one job seeker told researchers from Northeastern University. "My worthiness as a human and as an employee, as a worker, is based on my ability to filter myself through a series of automated gateways."

Only a handful of states have regulated the use of AI screening tools to make hiring decisions. Laws in Illinois, New Jersey, and Colorado (not yet in effect) prohibit employers from using discriminatory tools, but mandate little in the way of transparency beyond requiring employers to notify applicants that AI is being used. California's regulations are more robust, requiring employers to regularly test their AI hiring tools for bias. But none of those rules empower an individual to understand how a particular AI hiring tool judged them, or whether it discriminated against them.

So Markey went to work on an impossible task. He would spend the next six months writing emails, research papers, legal requests, and a constant stream of Python code, trying to peer inside the AI screener. "It turned into obsession," Markey told WIRED in February. "I don't think I've ever been this upset before in my life."

Markey's first medical training came in high school, when he sorted through the gallon ziplock bag where his father kept his prescription medications, recorded the names, and went to the local community college library to research their purposes. His dad was bipolar and addicted to alcohol, a charismatic, unpredictable ball of energy capable of showing great love and causing great pain.

One Christmas, which is also Markey's birthday, his father didn't show up because he'd been arrested for drunk driving. Another Christmas, Markey looked out the front window to find his truck being repossessed because his father had put it up as collateral for a payday loan. While Markey was away at college on Pell Grants, his family was forced to declare bankruptcy and lost their house. When he was 21, his father died.

Markey can recall the moment he became interested in pursuing psychiatry. It was when his father explained why he started drinking so heavily: In manic periods he would go days without sleeping, and the only thing that could force his eyes closed was a fifth of vodka. "It's just so sad to think if I said, 'Hey, let's go to a psychiatrist and get a low-dose Seroquel prescription and just have you sleep and address some of your mania,' like who knows what would happen?"

Markey had been preparing for a career on Wall Street. But after that conversation with his dad, he took a job in health care informatics and made plans to go to medical school. The summer before he started at Dartmouth in 2019, the stiffness he'd experienced in his back since he was a teenager grew worse and his pelvis began to feel like a cement block. By the end of his second year of school, Markey was laid flat by ankylosing spondylitis. He took a leave of absence, going from doctor to doctor seeking treatments that would allow him to continue with school.

During that same time, the Covid-19 pandemic was roiling the medical profession. Among myriad challenges, hospitals saw a massive increase in the number of applications for their residency programs. Prior to the pandemic, students typically had to travel to each hospital for interviews. When interviews went virtual, they could apply to dozens more programs than before. Markey applied to 82.

That surge has made it harder for hospitals to sort through and prioritize applications. In 2023, the Association of American Medical Colleges (AAMC) announced a partnership with Thalamus, the maker of a screening tool for residency applications called Cortex. Starting in 2025, the tool would be free to use for residency programs.
 
more

He Couldn't Land a Job Interview. Was AI to Blame?


It was mid-October, peak leaf-peeping season in Hanover, New Hampshire, and Chad Markey was on a rare break between clinical rotations during his last year of medical school. He should have been inhaling Green Mountain air and gossiping with his Dartmouth classmates about life after graduation. In a few months, they'd all be going their separate ways to start residency training at hospitals around... the country.

Instead, Markey was alone in his apartment, deep down a rabbit hole, preparing to go to war.

He'd wake each morning, eat breakfast, open his laptop at the kitchen table or settle into the tan armchair with the good back support, and start coding. Some days, he wouldn't notice the sun had gone down until one of his roommates came home and asked why the lights weren't on.

For days, Markey had been scrolling through a Discord group about medical residency, a font of crowdsourced knowledge where students report back to their peers on every stage of the application and selection process. He'd watched as other students, lots of them, posted about the interview invitations they'd received.

Markey didn't have any interview offers, only outright rejections. That seemed not just odd but wrong to the quiet-mannered 33-year-old from Houston, Texas, who speaks confidently about his accomplishments without bragging. He had good grades from an Ivy League medical school, author credits on articles in the Journal of the American Medical Association and The Lancet, a heart-wrenching personal statement, and glowing letters of recommendation. One professor wrote that they had "never met a medical student who is more skillful, talented, and appropriately situated in his pursuit of the field of medicine than Chad."

Markey combed through his application looking for a fatal flaw. He didn't find anything he thought would prompt a residency program director to toss an otherwise competitive application, so his suspicion turned to another culprit. He'd heard rumblings that some hospitals were using a free AI screening tool to help process applications -- and that it had been displaying incorrect grades for some students. He began to wonder whether AI was responsible for his lack of interview offers.

On the first page of his Medical Student Performance Evaluation, a comprehensive summary of his early career prepared by his school, Markey spotted language that he suspected might trigger an automated screening tool to downgrade his application. The MSPE stated that Markey had "voluntarily" taken three separate leaves of absence, totaling about 22 months, and had chosen to extend his third year of coursework over two years for "personal reasons."

That wasn't quite true. In 2021, Markey was diagnosed with ankylosing spondylitis, an autoimmune disease that affects the spine and could flare up to the point where he couldn't stand, much less do the intensive physical work expected of medical students during clinical rotations. He was on track to graduate from medical school in seven years, rather than the typical four, but his absences had been unavoidable and medically necessary. This was explained in a narrative paragraph on the first page. Calling the absences "voluntary," Markey felt, might be interpreted as evidence that he had succumbed to the pressure of medical school and not been able to keep up with his studies.

As the days went on, Markey said, he felt increasingly afraid that his years of training would end in failure. "I crawled out of a fucking black hole," he told WIRED, referring to his diagnosis. "I could not walk for six months. I've come this far, and this is happening?" He was asking himself the same question that pops into the minds of millions of other job seekers every day: Did an AI trash my application?

Even recruiters will admit it's fair to wonder. The CEO of a hiring platform said last fall that his industry is in "an AI doom loop": HR departments complain of a wave of AI-generated job applications, prompting the need for more AI filters. Applicants complain they're getting unfairly filtered out. Some fight AI with AI, filling their résumés and cover letters with buzzwords. "It feels very dystopian to me," one job seeker told researchers from Northeastern University. "My worthiness as a human and as an employee, as a worker, is based on my ability to filter myself through a series of automated gateways."

Only a handful of states have regulated the use of AI screening tools to make hiring decisions. Laws in Illinois, New Jersey, and Colorado (not yet in effect) prohibit employers from using discriminatory tools, but mandate little in the way of transparency beyond requiring employers to notify applicants that AI is being used. California's regulations are more robust, requiring employers to regularly test their AI hiring tools for bias. But none of those rules empower an individual to understand how a particular AI hiring tool judged them, or whether it discriminated against them.

So Markey went to work on an impossible task. He would spend the next six months writing emails, research papers, legal requests, and a constant stream of Python code, trying to peer inside the AI screener. "It turned into obsession," Markey told WIRED in February. "I don't think I've ever been this upset before in my life."

Markey's first medical training came in high school, when he sorted through the gallon ziplock bag where his father kept his prescription medications, recorded the names, and went to the local community college library to research their purposes. His dad was bipolar and addicted to alcohol, a charismatic, unpredictable ball of energy capable of showing great love and causing great pain.

One Christmas, which is also Markey's birthday, his father didn't show up because he'd been arrested for drunk driving. Another Christmas, Markey looked out the front window to find his truck being repossessed because his father had put it up as collateral for a payday loan. While Markey was away at college on Pell Grants, his family was forced to declare bankruptcy and lost their house. When he was 21, his father died.

Markey can recall the moment he became interested in pursuing psychiatry. It was when his father explained why he started drinking so heavily: In manic periods he would go days without sleeping, and the only thing that could force his eyes closed was a fifth of vodka. "It's just so sad to think if I said, 'Hey, let's go to a psychiatrist and get a low-dose Seroquel prescription and just have you sleep and address some of your mania,' like who knows what would happen?"

Markey had been preparing for a career on Wall Street. But after that conversation with his dad, he took a job in health care informatics and made plans to go to medical school. The summer before he started at Dartmouth in 2019, the stiffness he'd experienced in his back since he was a teenager grew worse and his pelvis began to feel like a cement block. By the end of his second year of school, Markey was laid flat by ankylosing spondylitis. He took a leave of absence, going from doctor to doctor seeking treatments that would allow him to continue with school.

During that same time, the Covid-19 pandemic was roiling the medical profession. Among myriad challenges, hospitals saw a massive increase in the number of applications for their residency programs. Prior to the pandemic, students typically had to travel to each hospital for interviews. When interviews went virtual, they could apply to dozens more programs than before. Markey applied to 82.

That surge has made it harder for hospitals to sort through and prioritize applications. In 2023, the Association of American Medical Colleges (AAMC) announced a partnership with Thalamus, the maker of a screening tool for residency applications called Cortex. Starting in 2025, the tool would be free to use for residency programs.

A handful of hospitals had already been working with Cortex, which displays application documents in an easily digestible dashboard and allows reviewers to search by keyword or filter applicants based on a wide variety of characteristics. Cortex also uses fine-tuned versions of OpenAI's generative models to standardize grades between schools with different practices. The AAMC partnership opened the door to broader adoption of the tool. According to Thalamus, about 1,500 residency programs around the country, or 30 percent, used Cortex to review applicants and make selection decisions during the 2025-2026 cycle.

Issues emerged within weeks of the September 2025 deadline when hospitals started reviewing applications. The company issued a statement saying some residency programs had reported that Cortex was displaying inaccurate grades for some people. In places like Markey's Discord group, the applicants chattered.

As Markey's anxiety about his lack of interviews was peaking, he got an exciting bit of news: A research abstract he'd submitted was accepted to be presented at the American Society of Hematology's upcoming annual meeting and simultaneously published in the journal Blood. What happened next deepened Markey's belief that AI systems, rather than humans, were responsible for his diminishing chances at getting into a residency program.

Markey already had 10 publications in medical journals on his résumé, but he began emailing his top-ranked residency programs to share the update about this latest accomplishment. The shift in his fortunes was immediate, he said.

Within an hour and 15 minutes of his first email to a residency program coordinator at one of the top psychiatry programs in the country, Markey received an exuberant response from the coordinator's boss. An interview offer followed less than an hour later, and they began to come in from Markey's other top choices too.

To Markey, it appeared to be "the first time they were seeing an application that hadn't even come across their desk." As he saw it at the time, "I was getting rejections because they had already filled up the top hundred slots based on the top hundred candidates that appear on the dashboard."

Just a couple days after Markey's epiphany, on October 16, Thalamus published a follow-up blog post about the previously reported issues with Cortex. The company said it had indeed documented inaccuracies in grades displayed to residency programs -- but only in 10 verified instances out of more than 4,000 customer inquiries. Cortex was now "99.3% accurate."

Thalamus later told WIRED that the company received no additional reports of inaccuracies out of more than 12,000 inquiries. But at the time, a lack of clarity around how Cortex employed AI sparked forum posts and journal articles. Steven Pletcher, a head and neck surgeon who oversees the otolaryngology residency program at the University of California San Francisco Hospital, told WIRED he heard from a colleague at another institution that some of the grades Cortex was displaying were "wildly inaccurate." Pletcher, who also conducts research into residency selection processes, wanted to investigate the platform himself.

"As a program director, when you hear, 'Hey we have this AI system for reviewing applications,' you think, can I just get it to give me a list of applicants that I should interview?" Pletcher told WIRED. "I had some concerns, I think as anyone would, if there's a new system for reviewing applications and it's presenting information inaccurately."

At a national meeting of the Society of University Otolaryngologists in November, Pletcher sat down with a colleague and reviewed applications in Cortex. One of the system's primary functions is the AI grade-normalization tool. From what Pletcher was seeing, the grades displayed for a given applicant on those charts could change from minute to minute.

Pletcher and four of his colleagues conducted a structured test and documented the errors they found. In January of this year, they published their results in the journal The Laryngoscope, describing "persistent errors in the Thalamus Cortex system with potential to negatively impact residency applicants and programs."

Jason Reminick, the CEO of Thalamus, told WIRED that many of the fears about Cortex expressed by students and medical schools in the 2025-2026 cycle were the result of misunderstandings about how the tool works. " A lot of the community suddenly had access to this and were playing with the tool without really going through the buying process," he said. "And I don't just mean the physical paying of money, I mean the exploratory process of understanding what the tool does."

Reminick told WIRED that besides an email from Pletcher, Thalamus received no other complaints about the grades displayed for students changing from minute to minute. He said the error was caused by the user moving too quickly between grade distribution graphs, resulting in the display briefly getting stuck. "This would not have affected any applicant's overall outcome" in the residency selection process, Reminick said. Thalamus requested that The Laryngoscope retract the article. The journal, which did not respond to WIRED's request for comment, has not done so.

As the day approached when med students would learn where they'd matched, Markey's own concerns about Cortex weren't going anywhere. In February, he reached out to Thalamus customer support to ask whether Cortex used information about leaves of absence to score candidates. "Whether anything affects an 'automatic score' or ordering depends on what that specific program has chosen to use for sorting/filtering," a Thalamus employee replied. "Programs can use different workflows and criteria, and we don't want to imply that one field (like [leave of absence] type) is universally used as a scoring input everywhere."

In a later statement to WIRED, Thalamus offered a clarification about Cortex's use of AI. "We understand that there is a large segment of our community understandably nervous about how quickly AI products are being rolled out and incorporated into every facet of society -- including sensitive use cases like medical students applying to residency programs," the statement said. The company said its approach has been transparent and cautious, but that "putting more emphasis on the limited AI tools would have been helpful to prevent misunderstandings about how AI was being used." According to Thalamus, "Not only is Cortex not a decision-making tool, it does not use AI to sort, filter, exclude, score, or rank applicants."

Of course, Markey hadn't heard any of that from Thalamus. As Match Day approached, all he had to go on was the February email he'd received, which he interpreted as indicating that "scoring" was at work. He still sensed AI bias -- and wanted to ferret it out.

Even for professional auditors with direct access to screening algorithms, it can be impossible to understand why an algorithm reached a particular conclusion, said Shea Brown, CEO of the auditing firm Babl AI. When a system runs on an LLM, it naturally has "a very opaque reasoning core at the center, and any kind of explainability about where it made a decision is hidden," he told WIRED. The only way to test for discrimination is in aggregate: Does the tool, for example, give measurably lower scores to equally qualified candidates with disabilities? "It can't be done causally based on a single person's application," Brown said.

The best a person can do in a situation like Markey's, where he suspected an AI system was picking up on specific language in his MSPE, is to test how an application performs with and without that language. That's where Markey started.

First, he ran three versions of his MSPE with slightly different language through a suite of AI fairness- and bias-testing tools that the AAMC recommends. The results indicated that a natural language processing algorithm might assess a sentence describing a leave of absence for "personal reasons" differently than a sentence that specified the leave was for a "medical condition," but Markey didn't like that the sample size was small and the test lacked context.

Next, he ran two versions of MSPE leave-of-absence language through VADER, an open-source natural language processing model that assigns emotional sentiment values to words and phrases, and found that a medically accurate description of his leaves of absence received a more positive sentiment score than the "personal reasons" language in his MSPE. He then used Python to create a synthetic dataset of 6,000 residency applicants. Each one was assigned test scores, grades, a count of how many publications they had on their résumé, and numeric rankings for how strong their letters of recommendation were and how well-suited they were for academic research. Markey then divided them into two cohorts -- one with sentiment analysis scores reflecting the leave-of-absence language in his MSPE and the other with scores reflecting medically accurate language.

The two groups were equally qualified, in terms of grades, test scores, and other characteristics. But when Markey ran the synthetic applicants through a logistic regression model trained to select the top 12 percent of applicants, those from the cohort with medically accurate MSPE language were 66 percent more likely to make the cut. Still, like his first test, this only shed light on how a generic algorithm might assess his application. Markey wanted to understand Thalamus' tools.

He tracked down the patent for an AI residency application screener built by the company Medicratic. Thalamus acquired Medicratic in 2025. Patents describe what a system may do, not necessarily what it does do, but it was the clearest explanation Markey could find of what might be happening inside the black box.

With the help of GitHub Copilot and eventually Anthropic's newly released Claude Code tool, Markey began to reverse engineer the system described in the Medicratic patent, mirroring the data pipeline and using the same open-source modules when he could. When necessary, he substituted Claude Code's advice and his own research. For example, before the system described in the patent can score applications, a residency program must indicate which characteristics -- such as academic performance, professionalism, or leadership -- it values most. Markey reviewed published research on residency selection and surveys of residency directors to determine how to weight those features.

Markey finished his system a few weeks before Match Day, March 20. He thought its outline and general features approximated how a tool like the one described in the Medicratic patent might process the same inputs. After more than four months dissecting various algorithms, it was the best he could do. Once again, when he ran different versions of his MSPE language through the system, there were starkly different results: Changing the wording about his leave of absence from "personal reasons" to a medically accurate description resulted in a significantly higher score.

That month, Markey sent Thalamus a data access request, under the New Hampshire Privacy Act, asking for all the personal data the company held about him. That included a comprehensive accounting of every document and data point that was input into Thalamus' systems about him; every preference parameter, weight, and scoring configuration applied to his application by residency programs; every score, attribute rating, and sentiment analysis calculated by Thalamus based on that data; and explanations of whether and how his data was processed to mitigate bias. Under the New Hampshire Privacy Act, the company had 45 days to respond.

WIRED contacted all of the residency programs Markey applied to and asked about their use of Cortex. Most didn't respond or declined to comment. Five programs replied that they hadn't used the tool. Yale New Haven Health told WIRED that its residency programs tried Cortex but stopped using it; a spokesperson declined to comment further. Two residency programs at Dartmouth Hitchcock Medical Center used Cortex to filter applications before program directors reviewed them, said Tennille Doyle, manager of graduate medical education programs, but most of the hospital's staff preferred to use their own screening methods.

Jeremy Walter, director of media relations at Temple Health, said one of the hospital's 59 residency programs used Cortex primarily to view applications during "manual screening," and "overall, we did not find the AI information very reliable." He declined to elaborate. According to Thalamus, multiple programs at Temple used Cortex during the recent selection cycle. "As with any new functionality, especially when introduced at scale, experiences can vary based on how features are used and interpreted," the company said.

Kari Roberts, who oversees graduate medical examination at Tufts Medical Center, told WIRED in an email that many of the school's residency programs tried Cortex for the first time last fall, using it to screen out any applications that were incomplete or failed to meet minimum requirements. "There were some significant errors in the algorithm that incorporated data from the MSPE, leading to wrong grade assignments," Roberts wrote. "This was not exclusive to our organization and was raised to the Thalamus team in real time by our dean's team." Thalamus told WIRED that "a very small number of identified discrepancies" were "investigated and corrected promptly" and that "in some of these cases, what was initially perceived as an inaccuracy was confirmed to be consistent with the source materials."

After Markey began cold-emailing program coordinators, he received interview offers from 10 institutions, including some of the most prestigious hospitals in the country. Ultimately he matched at Columbia University's psychiatry program at New York Presbyterian Hospital, where he will begin his residency in July.

Three days after he got matched, Markey received a response from Thalamus to his data access request. The company's chief of staff, Michele Li, wrote that none of the programs he had applied to had used the Medicratic tool that Markey had been attempting to reverse engineer. Cortex itself didn't use the sentiment-scoring methodology described in the patent.

Reminick, Thalamus' CEO, confirmed to WIRED that during the 2025-2026 cycle, Cortex did not algorithmically score or rank applicants. The tool primarily uses AI for grade normalization and to display a badge indicating whether an applicant is interested in academic research, he said. However, Thalamus plans to pilot an AI screener that will allow residency programs to create candidate profiles and then assess how well applicants match those profiles, Reminick said. During the pilot, applicants will have to opt in to the screening.

Even after matching at Columbia and receiving the letter from Thalamus denying his suspicions about his own applications, Markey said he doesn't regret the months he devoted to unpacking screening tools. " I'm very grateful for where I've gotten, so when things threaten that, I want to make sure I'm responding correctly," he said. In fact, he has continued his investigation of how large language models pick up on semantic signals in job application material and embed them down the pipeline into decisions or recommendations.

There is proof, even in the world of AI hiring tools, that some form of due process, however imperfect, can be built and regulated into these systems. One of the most popular applications of AI in human resources is to conduct background checks. Companies like Checkr automate the process for millions of applications monthly, comparing candidate names against public records for any evidence of disqualifying criminal activity. A lot of the time, these systems make mistakes that cost people jobs.

But background-check companies, whether they use humans or AI, are subject to provisions in the federal Fair Credit Reporting Act that require them to share the results of a background check with the job candidate upon request, conduct an investigation if the accuracy of the background check is disputed, and send the job candidate the written results of that investigation. Job candidates can win or settle individual and class action lawsuits against background-check companies that provide inaccurate reports.

It's a system with many of its own problems, but it at least offers individual job seekers an option other than screaming helplessly into the void. Not everyone should need to be an Ivy League medical student with a background in informatics and coding and a massive axe to grind.
 
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Medical School Applicant Suspects AI Bias in Residency Rejections


A medical school graduate with strong credentials suspects an AI screening tool may be unfairly impacting his chances of securing a residency, leading him to investigate the issue after receiving only rejections despite a competitive application.

Instead, Markey was alone in his apartment, deep down a rabbit hole, preparing to go to war. He'd wake each morning, eat breakfast, open his laptop at... the kitchen table or settle into the tan armchair with the good back support, and start coding.

Some days, he wouldn't notice the sun had gone down until one of his roommates came home and asked why the lights weren't on. For days, Markey had been scrolling through a Discord group about medical residency, a font of crowdsourced knowledge where students report back to their peers on every stage of the application and selection process. He'd watched as other students, lots of them, posted about the interview invitations they'd received.

Markey didn't have any interview offers, only outright rejections. That seemed not just odd but wrong to the quiet-mannered 33-year-old from Houston, Texas, who speaks confidently about his accomplishments without bragging. He had good grades from an Ivy League medical school, author credits on articles in the Journal of the American Medical Association and The Lancet, a heart-wrenching personal statement, and glowing letters of recommendation.

One professor wrote that they had "never met a medical student who is more skillful, talented, and appropriately situated in his pursuit of the field of medicine than Chad. " Markey combed through his application looking for a fatal flaw. He didn't find anything he thought would prompt a residency program director to toss an otherwise competitive application, so his suspicion turned to another culprit.

He'd heard rumblings that some hospitals were using a free AI screening tool to help process applications -- and that it had been displaying incorrect grades for some students. He began to wonder whether AI was responsible for his lack of interview offers. On the first page of his Medical Student Performance Evaluation, a comprehensive summary of his early career prepared by his school, Markey spotted language that he suspected might trigger an automated screening tool to downgrade his application.

The MSPE stated that Markey had "voluntarily" taken three separate leaves of absence, totaling about 22 months, and had chosen to extend his third year of coursework over two years for "personal reasons. " That wasn't quite true. In 2021, Markey was diagnosed with ankylosing spondylitis, an autoimmune disease that affects the spine and could flare up to the point where he couldn't stand, much less do the intensive physical work expected of medical students during clinical rotations.

He was on track to graduate from medical school in seven years, rather than the typical four, but his absences had been unavoidable and medically necessary. This was explained in a narrative paragraph on the first page. Calling the absences "voluntary," Markey felt, might be interpreted as evidence that he had succumbed to the pressure of medical school and not been able to keep up with his studies.

As the days went on, Markey said, he felt increasingly afraid that his years of training would end in failure.

"I crawled out of a fucking black hole," he told WIRED, referring to his diagnosis. "I could not walk for six months. I've come this far, and this is happening? " He was asking himself the same question that pops into the minds of millions of other job seekers every day: Did an AI trash my application?

Even recruiters will admit it's fair to wonder. The CEO of a hiring platform said last fall that his industry is in "an AI doom loop": HR departments complain of a wave of AI-generated job applications, prompting the need for more AI filters. Applicants complain they're getting unfairly filtered out. Some fight AI with AI, filling their résumés and cover letters with buzzwords.

"It feels very dystopian to me," one job seeker told researchers from Northeastern University. "My worthiness as a human and as an employee, as a worker, is based on my ability to filter myself through a series of automated gateways. " Only a handful of states have regulated the use of AI screening tools to make hiring decisions.

Laws in Illinois, New Jersey, and Colorado prohibit employers from using discriminatory tools, but mandate little in the way of transparency beyond requiring employers to notify applicants that AI is being used. California's regulations are more robust, requiring employers to regularly test their AI hiring tools for bias. But none of those rules empower an individual to understand how a particular AI hiring tool judged them, or whether it discriminated against them.

So Markey went to work on an impossible task. He would spend the next six months writing emails, research papers, legal requests, and a constant stream of Python code, trying to peer inside the AI screener.

"It turned into obsession," Markey told WIRED in February. "I don't think I've ever been this upset before in my life. " One Christmas, which is also Markey's birthday, his father didn't show up because he'd been arrested for drunk driving. Another Christmas, Markey looked out the front window to find his truck being repossessed because his father had put it up as collateral for a payday loan.

While Markey was away at college on Pell Grants, his family was forced to declare bankruptcy and lost their house. When he was 21, his father died. Markey can recall the moment he became interested in pursuing psychiatry. It was when his father explained why he started drinking so heavily: In manic periods he would go days without sleeping, and the only thing that could force his eyes closed was a fifth of vodka.

"It's just so sad to think if I said, 'Hey, let's go to a psychiatrist and get a low-dose Seroquel prescription and just have you sleep and address some of your mania,' like who knows what would happen? " Markey had been preparing for a career on Wall Street. But after that conversation with his dad, he took a job in health care informatics and made plans to go to medical school.

The summer before he started at Dartmouth in 2019, the stiffness he'd experienced in his back since he was a teenager grew worse and his pelvis began to feel like a cement block. By the end of his second year of school, Markey was laid flat by ankylosing spondylitis. He took a leave of absence, going from doctor to doctor seeking treatments that would allow him to continue with school.

During that same time, the Covid-19 pandemic was roiling the medical profession. Among myriad challenges, hospitals saw a massive increase in the number of applications for their residency programs. Prior to the pandemic, students typically had to travel to each hospital for interviews. When interviews went virtual, they could apply to dozens more programs than before.

Markey applied to 82. That surge has made it harder for hospitals to sort through and prioritize applications. In 2023, the Association of American Medical Colleges announced a partnership with Thalamus, the maker of a screening tool for residency applications called Cortex. Starting in 2025, the tool would be free to use for residency programs.

A handful of hospitals had already been working with Cortex, which displays application documents in an easily digestible dashboard and allows reviewers to search by keyword or filter applicants based on a wide variety of characteristics. Cortex also uses fine-tuned versions of OpenAI's generative models to standardize grades between schools with different practices. The AAMC partnership opened the door to broader adoption of the tool.

According to Thalamus, about 1,500 residency programs around the country, or 30 percent, used Cortex to review applicants and make selection decisions during the 2025-2026 cycle. Issues emerged within weeks of the September 2025 deadline when hospitals started reviewing applications. The company issued a statement saying some residency programs had reported that Cortex was displaying inaccurate grades for some people. In places like Markey's Discord group, the applicants chattered.

As Markey's anxiety about his lack of interviews was peaking, he got an exciting bit of news: A research abstract he'd submitted was accepted to be presented at the American Society of Hematology's upcoming annual meeting and simultaneously published in the journal Blood. What happened next deepened Markey's belief that AI systems, rather than humans, were responsible for his diminishing chances at getting into a residency program.

Markey already had 10 publications in medical journals on his résumé, but he began emailing his top-ranked residency programs to share the update about this latest accomplishment. The shift in his fortunes was immediate, he said. Within an hour and 15 minutes of his first email to a residency program coordinator at one of the top psychiatry programs in the country, Markey received an exuberant response from the coordinator's boss.

An interview offer followed less than an hour later, and they began to come in from Markey's other top choices too. To Markey, it appeared to be "the first time they were seeing an application that hadn't even come across their desk. " As he saw it at the time, "I was getting rejections because they had already filled up the top hundred slots based on the top hundred candidates that appear on the dashboard.

" Thalamus later told WIRED that the company received no additional reports of inaccuracies out of more than 12,000 inquiries. But at the time, a lack of clarity around how Cortex employed AI sparked forum posts and journal articles.

Steven Pletcher, a head and neck surgeon who oversees the otolaryngology residency program at the University of California San Francisco Hospital, told WIRED he heard from a colleague at another institution that some of the grades Cortex was displaying were "wildly inaccurate. " Pletcher, who also conducts research into residency selection processes, wanted to investigate the platform himself.

"As a program director, when you hear, 'Hey we have this AI system for reviewing applications,' you think, can I just get it to give me a list of applicants that I should interview? " Pletcher told WIRED.

"I had some concerns, I think as anyone would, if there's a new system for reviewing applications and it's presenting information inaccurately. " At a national meeting of the Society of University Otolaryngologists in November, Pletcher sat down with a colleague and reviewed applications in Cortex. One of the system's primary functions is the AI grade-normalization tool. From what Pletcher was seeing, the grades displayed for a given applicant on those charts could change from minute to minute.

Pletcher and four of his colleagues conducted a structured test and documented the errors they found. In January of this year, they published their results in the journal The Laryngoscope, describing "persistent errors in the Thalamus Cortex system with potential to negatively impact residency applicants and programs.

" Jason Reminick, the CEO of Thalamus, told WIRED that many of the fears about Cortex expressed by students and medical schools in the 2025-2026 cycle were the result of misunderstandings about how the tool works.

" A lot of the community suddenly had access to this and were playing with the tool without really going through the buying process," he said. "And I don't just mean the physical paying of money, I mean the exploratory process of understanding what the tool does. " Reminick told WIRED that besides an email from Pletcher, Thalamus received no other complaints about the grades displayed for students changing from minute to minute.

He said the error was caused by the user moving too quickly between grade distribution graphs, resulting in the display briefly getting stuck.

"This would not have affected any applicant's overall outcome" in the residency selection process, Reminick said. Thalamus requested that The Laryngoscope retract the article. The journal, which did not respond to WIRED's request for comment, has not done so. In a later statement to WIRED, Thalamus offered a clarification about Cortex's use of AI.

"We understand that there is a large segment of our community understandably nervous about how quickly AI products are being rolled out and incorporated into every facet of society -- including sensitive use cases like medical students applying to residency programs," the statement said. The company said its approach has been transparent and cautious, but that "putting more emphasis on the limited AI tools would have been helpful to prevent misunderstandings about how AI was being used.

" According to Thalamus, "Not only is Cortex not a decision-making tool, it does not use AI to sort, filter, exclude, score, or rank applicants. " Of course, Markey hadn't heard any of that from Thalamus. As Match Day approached, all he had to go on was the February email he'd received, which he interpreted as indicating that "scoring" was at work. He still sensed AI bias -- and wanted to ferret it out.

Even for professional auditors with direct access to screening algorithms, it can be impossible to understand why an algorithm reached a particular conclusion, said Shea Brown, CEO of the auditing firm Babl AI. When a system runs on an LLM, it naturally has "a very opaque reasoning core at the center, and any kind of explainability about where it made a decision is hidden," he told WIRED.

The only way to test for discrimination is in aggregate: Does the tool, for example, give measurably lower scores to equally qualified candidates with disabilities?

"It can't be done causally based on a single person's application," Brown said. The best a person can do in a situation like Markey's, where he suspected an AI system was picking up on specific language in his MSPE, is to test how an application performs with and without that language. That's where Markey started.

First, he ran three versions of his MSPE with slightly different language through a suite of AI fairness- and bias-testing tools that the AAMC recommends. The results indicated that a natural language processing algorithm might assess a sentence describing a leave of absence for "personal reasons" differently than a sentence that specified the leave was for a "medical condition," but Markey didn't like that the sample size was small and the test lacked context.

Next, he ran two versions of MSPE leave-of-absence language through VADER, an open-source natural language processing model that assigns emotional sentiment values to words and phrases, and found that a medically accurate description of his leaves of absence received a more positive sentiment score than the "personal reasons" language in his MSPE. He then used Python to create a synthetic dataset of 6,000 residency applicants.

Each one was assigned test scores, grades, a count of how many publications they had on their résumé, and numeric rankings for how strong their letters of recommendation were and how well-suited they were for academic research. Markey then divided them into two cohorts -- one with sentiment analysis scores reflecting the leave-of-absence language in his MSPE and the other with scores reflecting medically accurate language. The two groups were equally qualified, in terms of grades, test scores, and other characteristics.

But when Markey ran the synthetic applicants through a logistic regression model trained to select the top 12 percent of applicants, those from the cohort with medically accurate MSPE language were 66 percent more likely to make the cut. Still, like his first test, this only shed light on how a generic algorithm might assess his application. Markey wanted to understand Thalamus' tools. He tracked down the patent for an AI residency application screener built by the company Medicratic.

Thalamus acquired Medicratic in 2025. Patents describe what a system may do, not necessarily what it does do, but it was the clearest explanation Markey could find of what might be happening inside the black box. With the help of GitHub Copilot and eventually Anthropic's newly released Claude Code tool, Markey began to reverse engineer the system described in the Medicratic patent, mirroring the data pipeline and using the same open-source modules when he could.

When necessary, he substituted Claude Code's advice and his own research. For example, before the system described in the patent can score applications, a residency program must indicate which characteristics -- such as academic performance, professionalism, or leadership -- it values most. Markey reviewed published research on residency selection and surveys of residency directors to determine how to weight those features. Markey finished his system a few weeks before Match Day, March 20.

He thought its outline and general features approximated how a tool like the one described in the Medicratic patent might process the same inputs. After more than four months dissecting various algorithms, it was the best he could do. Once again, when he ran different versions of his MSPE language through the system, there were starkly different results: Changing the wording about his leave of absence from "personal reasons" to a medically accurate description resulted in a significantly higher score.

That month, Markey sent Thalamus a data access request, under the New Hampshire Privacy Act, asking for all the personal data the company held about him. That included a comprehensive accounting of every document and data point that was input into Thalamus' systems about him; every preference parameter, weight, and scoring configuration applied to his application by residency programs; every score, attribute rating, and sentiment analysis calculated by Thalamus based on that data; and explanations of whether and how his data was processed to mitigate bias.

Under the New Hampshire Privacy Act, the company had 45 days to respond. Jeremy Walter, director of media relations at Temple Health, said one of the hospital's 59 residency programs used Cortex primarily to view applications during "manual screening," and "overall, we did not find the AI information very reliable. " He declined to elaborate. According to Thalamus, multiple programs at Temple used Cortex during the recent selection cycle.

"As with any new functionality, especially when introduced at scale, experiences can vary based on how features are used and interpreted," the company said. Kari Roberts, who oversees graduate medical examination at Tufts Medical Center, told WIRED in an email that many of the school's residency programs tried Cortex for the first time last fall, using it to screen out any applications that were incomplete or failed to meet minimum requirements.

"There were some significant errors in the algorithm that incorporated data from the MSPE, leading to wrong grade assignments," Roberts wrote. "This was not exclusive to our organization and was raised to the Thalamus team in real time by our dean's team.

" Thalamus told WIRED that "a very small number of identified discrepancies" were "investigated and corrected promptly" and that "in some of these cases, what was initially perceived as an inaccuracy was confirmed to be consistent with the source materials. " Three days after he got matched, Markey received a response from Thalamus to his data access request.

The company's chief of staff, Michele Li, wrote that none of the programs he had applied to had used the Medicratic tool that Markey had been attempting to reverse engineer. Cortex itself didn't use the sentiment-scoring methodology described in the patent. Reminick, Thalamus' CEO, confirmed to WIRED that during the 2025-2026 cycle, Cortex did not algorithmically score or rank applicants.

The tool primarily uses AI for grade normalization and to display a badge indicating whether an applicant is interested in academic research, he said. However, Thalamus plans to pilot an AI screener that will allow residency programs to create candidate profiles and then assess how well applicants match those profiles, Reminick said. During the pilot, applicants will have to opt in to the screening.

Even after matching at Columbia and receiving the letter from Thalamus denying his suspicions about his own applications, Markey said he doesn't regret the months he devoted to unpacking screening tools.

" I'm very grateful for where I've gotten, so when things threaten that, I want to make sure I'm responding correctly," he said. In fact, he has continued his investigation of how large language models pick up on semantic signals in job application material and embed them down the pipeline into decisions or recommendations.

There is proof, even in the world of AI hiring tools, that some form of due process, however imperfect, can be built and regulated into these systems. One of the most popular applications of AI in human resources is to conduct background checks. Companies like Checkr automate the process for millions of applications monthly, comparing candidate names against public records for any evidence of disqualifying criminal activity. A lot of the time, these systems make mistakes that cost people jobs.

But background-check companies, whether they use humans or AI, are subject to provisions in the federal Fair Credit Reporting Act that require them to share the results of a background check with the job candidate upon request, conduct an investigation if the accuracy of the background check is disputed, and send the job candidate the written results of that investigation. Job candidates can win or settle individual and class action lawsuits against background-check companies that provide inaccurate reports.

It's a system with many of its own problems, but it at least offers individual job seekers an option other than screaming helplessly into the void. Not everyone should need to be an Ivy League medical student with a background in informatics and coding and a massive axe to grind. Let us know what you think about this article. Submit a letter to the editor at mail@wired.com.

AI Medical Residency Application Process Algorithms Bias

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How To Get A Job In 10 Ways


Kate Hudson teaches us as Andie Anderson, that dating is all about playing your cards right. The same might be true for job hunting, except I know more about the interview process than I do about men.

With summer coming up, the daunting task of getting a job might be fresh on your mind. But, don't stress, we've all been there! In fact, my house of six college girls have been scouring the job... market these past few months, and I am happy to announce that we all have found the "one!" So don't play hard to get... Start putting yourself out there! Here's how to get a job in 10 ways:

before the interview...

The cold, hard truth of job hunting is that it's 100% easier to become employed if you know someone who knows someone. Networking is the #1 way to at least guarantee an interview. Don't be shy to inquire with your friends or mutual friends about if their work is hiring. People want to help you more than you think! I landed my dream internship by reaching out to a mom I babysat for!

The age-old method that your parents always tell you about actually does work! Employers are more likely to give an interview to someone who makes the time to show up. It also eliminates the competition between online applicants. Note that if this is not possible, try to find your desired employer's email and express your personalized interest there!

Although it's common to have your heart set on a dream job or company, always keep an open mind. Don't put all your eggs in one basket, and branch out! This will make certain rejections easier (try not to take it personally).

Once you land your interview...

Although it may seem obvious, it's always a good idea to come prepared. Do your research. Make sure you know a little about the company and what your strong suits are. Think ahead about the questions your interviewer might ask.

Common ones include, "Tell me about yourself," "What are you looking for in a workplace?" "What is your biggest weakness?" and "How do you work in a team?" And don't forget that it goes both ways! Always come prepared with a few thoughtful questions to ask them at the end.

I know it may be deemed common knowledge, but make sure you have a go-to professional outfit in your wardrobe. It's better to come overdressed to your interview. It's also helpful to match the vibe or wherever you're applying (i.e., ballet flats for an office, boots for a bar/restaurant). My favorite interview outfit consists of a black work dress and tan Frye boots (works like a charm).

First impressions are important! Employers want someone who shows up to work with a positive attitude. Between you and me, it's okay to fake it until you make it. Think of an interview as a time to talk about your success, an opportunity you don't often have! Always act like the most enthusiastic person in the room in order to ensure that you are a positive, trustworthy employee. Who doesn't want to hire someone who has their best interests?

You can talk about your resume for as long as you would like, but what really differentiates you is your ability to show what you have learned from your past experiences. Although it is obviously important to keep it professional, don't be afraid to be candid with your interviewer.

For example, I often discuss the work environment that I didn't particularly enjoy, comparing it to an environment I did, and add what I have noticed about what leadership or attitude made it run differently. It can also be as simple as saying you have learned the correct language for customer experience.

Whatever pertains to your experience, and how it has made you grow for the better! This will make your interviewer pinpoint you as an insightful problem-solver.

In every single job interview, I have always been asked the same question: How do you work in a team environment? It's very important to emphasize that you want to show up and be a good coworker for others. It's essential to reference certain situations where working in teams guarantees group success. Make sure you have a certain story on hand.

Availability is everything, especially for part-time jobs. Tell your employer you can start as soon as possible, and you want to work as much as possible given your schedule. No boss wants to hire someone who can work one day a week.

Honestly, it's okay to stretch the truth in this situation and say you're a bit more available than you really are, especially during the interview stage. Make sure to block out your class times, but you can always change your availability later!

Last but certainly not least, always follow up after your interview. Don't be afraid to shoot an email expressing your gratitude and enthusiasm. A quick thank-you email keeps you fresh in their mind and shows professionalism.

Now that you've read these tips and tricks, I hope you feel confident in putting yourself on the market! Andie Anderson would certainly approve. Best of luck finding the job of your dreams!
 
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