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  • in hiring
  • 7 min read

Interviewing in the age of AI

Interviews have always been a bad proxy. You get maybe an hour with someone and you're supposed to figure out whether they'll be effective in a role that plays out over months and years. You can't replicate real working conditions—the codebase they'd actually work in, the team dynamics, the ambiguity of real problems. So you construct artificial scenarios and hope the signal transfers.

That fundamental challenge hasn't changed. However, AI has made the gaps in our proxies impossible to ignore.

The signal problem

The core question in any interview is: can this person actually do the job? Everything else—the whiteboard problems, the take-homes, the system design rounds—is just scaffolding to get at that question indirectly.

But with AI, it's now possible to offload much of the thinking and problem-solving itself, making the assessment even harder.

When someone submits a clean take-home with sensible architecture and thorough tests, one used to be able to assume a baseline of understanding behind it. This is no longer true. Not because the code is bad—it's often excellent. But GitHub Copilot, Claude, and ChatGPT have converged on identical patterns. A few years ago, messy but functional code suggested a real engineer working under pressure. Now, too-perfect code could be the tell, but penalising clean code is obviously absurd.

At the same time banning AI isn't the answer. I want engineers using AI. It's the most significant productivity tool to hit software engineering in decades, and anyone not using it is leaving value on the table. The question I'm actually trying to answer in an interview is "can you think with AI, or are you just deferring to it?"

Old formats, honest limitations

These interview formats didn't suddenly break. They always had limitations as proxies for real work. AI just made those limitations undeniable.

Long take-home exercises were always a noisy signal. A four-hour project tells you someone can deliver polished work with unlimited resources and no time pressure—which is rarely what the actual job looks like. AI turned the noise up to eleven: now the output mostly tells me the candidate has access to coding tools. Table stakes.

LeetCode-style problems were always testing a narrow skill—pattern recognition and algorithmic recall—that correlates weakly with day-to-day engineering. AI happens to be exceptionally good at exactly this narrow skill, so now I can't even get the weak signal I used to.

Anything with a "correct answer" has this problem. The clearer the specification, the easier it is for AI to solve. Which is ironic—we used to think clear specs made for fair interviews, which they did. They also made for easy prompts.

I'm not saying these formats are worthless. But the signal they produce has shifted from "can this person solve problems?" to something murkier. And rather than trying to salvage them, I'd ask: what formats actually test the thing I care about?

What I'm looking for now

The formats I've been experimenting with share a common thread: they test whether someone can think, not whether they can produce output. AI is great at producing output, but thinking, judging and validating is still a human job.

Shorter exercises + longer conversations

Instead of a four-hour take-home, a thirty-minute exercise followed by forty-five minutes of discussion. The code is a starting point, not the deliverable.

Why did you structure it this way? What would you change if the requirements shifted to X? Where would this break at scale? What's the ugliest part of this code?

AI can generate code. It can't explain the tradeoffs you considered and rejected. It can't tell me about the moment you started down one path, realised it was wrong, and backed out. And I also just ask directly: how did you use AI? That question alone is surprisingly revealing. Someone who used AI well can articulate what they delegated, what they modified, and what they rejected. Someone who deferred to it entirely tends to get vague.

Those conversations reveal thinking—including whether someone used AI effectively as a tool versus blindly accepting its first suggestion.

Live investigation instead of live coding

Writing code from scratch under interview pressure was always a weird skill to test. It didn't map well to real work even before AI.

Investigation is different. I give candidates a system that's misbehaving—not a syntax error, something behavioural. A race condition. A caching issue. A misunderstood API contract. And yes, they can use whatever tools they want, including AI.

What I'm watching isn't whether they can find the bug. Claude Code can find bugs. What I'm watching is everything around the finding: how they scope the problem, what questions they ask before touching the code, which hypotheses they form first, what they choose to validate versus take on faith.

The person directing the investigation matters more than the investigation itself. A strong engineer will use AI to speed up the search but still decide where to search. They'll sanity-check the AI's suggestion against their own understanding rather than blindly applying a fix. They'll know when the tool is confidently wrong.

Someone who's genuinely thinking will say things like "that can't be the issue because X" or "let me verify this assumption first." Someone who's outsourced the thinking will paste the error into a chat window and accept whatever comes back.

Watching someone use AI well during an interview is actually one of the strongest positive signals I've found. When a candidate uses AI to quickly test a hypothesis, then critically evaluates the result and adjusts course — that's exactly the workflow I want to see on the job.

System design with real constraints

AI is great at generating architecture diagrams and textbook answers. It's less great at navigating the messy reality of your specific situation.

When I ask about system design, I focus on constraints. What if we need to support 10x the traffic? What if the team is two people? What if we need to ship in four weeks? What if this has to run in air-gapped environments?

Good engineers make different choices in different contexts. They can explain why this context changes the answer. Someone who's genuinely thinking—whether or not they used AI to explore options—will navigate these pivots fluidly. Someone who's outsourced the thinking will flounder when the constraints shift.

Roleplay scenarios

This is the most experimental—but possibly the most promising.

FDE and customer-facing engineering roles need skills that are fundamentally about human judgment: real-time conversation, reading the room, managing frustrated stakeholders, diagnosing problems under pressure.

I've started using roleplay scenarios where I play a customer with a problem, and the candidate has to figure out what's actually wrong—not what I say is wrong.

Concrete example: The Broken Dashboard

Here's the shape of an interview I've been running lately.

I play a frustrated stakeholder—a senior executive at a large organisation. Something is wrong with a dashboard that feeds into a critical business process. The numbers don't match what another team is reporting. There's time pressure. The candidate's job is to help me figure out what's going on.

The scenario is designed so that the obvious explanation ("the dashboard is broken, fix the code") is wrong. The real root causes are subtler—the kind of thing you'd only uncover by asking careful questions about data sources, definitions, and upstream processes. There's no bug to patch. The discrepancy is fully explainable, but only if you resist the urge to jump to conclusions.

I won't give away more than that—I plan to continue using this interview.

What this tests

Problem diagnosis under pressure. Does the candidate immediately promise to "fix" the thing, or do they slow down and figure out what's actually happening?

Customer communication. Can they manage a frustrated stakeholder while still asking clarifying questions? Do they resist the urge to commit to solutions before understanding the problem?

Data literacy. Do they think to ask about how the numbers are generated? Or do they assume the system is broken because that's what the customer said?

Ownership. When they figure out the root cause, do they offer a path forward—both for the immediate crisis and the longer-term fix?

Why this works

This isn't a prompt you can give to ChatGPT. There's no code to generate. The "answer" emerges through conversation—through noticing that the stakeholder doesn't actually understand how the system works, through asking the right diagnostic questions, through realising that different teams might be measuring different things.

It tests the thing that actually matters in deployment roles: can you figure out what's really going on, communicate clearly under pressure, and move towards a resolution? Those skills don't change regardless of what tools you're using—and they're the hardest to fake.

What I'm still figuring out

I won't pretend I've cracked this. Some open questions:

Consistency. Roleplay scenarios are harder to evaluate objectively than coding tests. Different interviewers might reach different conclusions about the same conversation.

Fairness. These approaches favour candidates who are comfortable thinking out loud, explaining their reasoning, engaging in back-to-back. That might disadvantage candidates who are brilliant but less verbally fluent.

Scalability. A forty-five-minute investigation exercise with live observation doesn't scale like a take-home. You need more interviewers, more coordination.

AI will keep improving. Maybe next year there's an AI that can roleplay its way through a customer scenario. Maybe debugging exercises become as compromised as LeetCode. I expect to keep iterating on this—the target is always moving.

The goal hasn't changed

I want to hire people who can do the job. The job now includes using AI effectively—so I'm not designing interviews to exclude AI. Instead, I'm designing interviews that reveal whether someone can think, whether or not they have AI in the room.

The best engineers I work with use AI constantly. They also know when to override it, when to dig deeper, when the AI's confident answer is confidently wrong. That judgment is what I'm interviewing for.

I think interviews are heading somewhere interesting. The formats that survive will be the ones that test what AI can't fake: genuine understanding, real-time judgment, and the ability to navigate ambiguity with another human. The interviews of five years from now will look less like exams and more like working sessions — because that's what they should have been all along.

  • in hiring
  • 5 min read

The Database Selection Trap: Why Your Technical Interviews Might Be Testing the Wrong Things

I recently watched a talented engineer fail a system design interview, and it made me question everything I thought I knew about technical hiring.

The candidate was asked to design a data model for a food delivery platform. They chose PostgreSQL. When the requirements evolved—millions of drivers, real-time location updates, flexible schemas—they couldn't pivot to NoSQL. Despite perfect nudges from the interviewer, they remained stuck.

Here's what haunted me: In any real engineering role, this person would have thrived. They'd have teammates suggesting alternatives. They'd have design reviews. They'd have documentation and prior art to reference.

But in that interview room, artificially isolated from every resource that makes modern engineering possible, they failed.

This isn't a story about lowering the bar. It's about recognizing that many of our "standard" technical interviews are testing the wrong things entirely.

The Comfort of Cargo Cult Interviews

We've all been there. You're tasked with building a hiring process, so you do what seems logical: look at what successful companies do and copy it. Google does system design interviews? So do we. Facebook does algorithm challenges? Add it to the list.

But here's the problem: we copy the form without understanding the function.

That database selection question? It made perfect sense... until I asked myself what we were actually testing: - Can this person independently choose the right database in isolation? - Or can this person build great systems in a collaborative environment?

These are fundamentally different skills. And only one of them matters for the job.

The Three Interview Traps That Filter Out Great Engineers

After auditing dozens of hiring processes, I've identified three common traps that eliminate potentially excellent engineers for the wrong reasons:

1. The Isolation Trap

The Setup: Candidate must solve everything alone, from first principles, without any external resources.

The Problem: This isn't how engineering works. Ever. Modern engineering is collaborative, iterative, and builds on existing knowledge. The best engineers aren't those who can reinvent everything in isolation—they're those who can leverage their team and tools effectively.

Real Example: A senior engineer with 10 years of experience couldn't remember the exact syntax for a specific PostgreSQL window function. In reality, they'd look it up in 30 seconds. In the interview, they struggled for 10 minutes and lost confidence.

2. The Perfection Trap

The Setup: One significant stumble means failure, regardless of overall performance.

The Problem: Engineering is about recovery and iteration, not perfection. Some of the best engineers I've worked with are great precisely because they recognize mistakes quickly and course-correct effectively. But our interviews often punish any deviation from the "perfect" answer.

Real Example: A candidate designed 90% of an excellent solution but made one architectural decision that would have caused scaling issues. Instead of seeing if they could identify and fix it with feedback (like they would in a real design review), they were marked down significantly.

3. The Specific Knowledge Trap

The Setup: Testing specific technical knowledge rather than fundamental thinking.

The Problem: Technology changes. What matters is engineering judgment, learning ability, and problem-solving approach. But we often test whether someone memorized the specific technologies we happen to use today.

Real Example: A brilliant engineer "failed" because they weren't familiar with Kafka. They understood event-driven architectures perfectly and had used RabbitMQ extensively. Given a week on the job, they'd be productive with Kafka. But the interview didn't capture that.

A Better Way: Design Interviews That Mirror Reality

The solution isn't to make interviews easier. It's to make them more realistic. Here's a framework I use with my clients:

Step 1: Start With Role Reality

Before designing any interview, answer these questions: - What does a typical day look like for this engineer? - What resources do they have access to? - How do they collaborate with others? - What does "great performance" actually look like?

Step 2: Map Backwards to Interview Signals

For each critical skill, ask: - What's the minimal signal we need to assess this? - How can we test this in a way that mirrors reality? - What support would they have in the real role?

Step 3: Build in Collaboration and Iteration

Instead of testing isolated perfection, test realistic excellence: - Allow candidates to ask clarifying questions (like they would with stakeholders) - Provide feedback and see how they incorporate it (like in code review) - Let them reference documentation for syntax (like they would with Google) - Focus on their thinking process, not memorized solutions

Case Study: Redesigning the System Design Interview

Here's how we transformed that problematic database interview:

Old Version: "Design a data model for a food delivery system. Choose your database and justify it."

New Version: "Let's design a data model for a food delivery system together. Here's our current scale and requirements. As we go, I'll play the role of your teammate and share what we've learned from our existing systems."

The key changes: 1. Collaborative framing - "together" and "teammate" set the tone 2. Living requirements - Requirements evolve during the discussion, like real projects 3. Historical context - They can ask about existing systems and constraints 4. Focus on reasoning - We care more about how they think through trade-offs than their initial choice

The result? We started identifying engineers who would excel in our actual environment, not those who could perform in artificial interview conditions.

The Hidden Cost of Bad Interviews

Every time we filter out a great engineer because they stumbled on an artificial constraint, we're not just losing a potential hire. We're: - Reinforcing biases toward certain backgrounds (those who've practiced these specific interview formats) - Extending our hiring timeline as we search for unicorns who excel at interviews AND engineering - Building teams that optimize for interview performance over actual job performance

Your Next Step: The One-Question Audit

Pick one question from your current interview process. Just one. Now ask yourself:

"If a strong engineer failed this specific question but excelled at everything else, would I bet they'd fail in the actual role?"

If the answer is no, you're testing the wrong thing.

The Path Forward

Great hiring isn't about finding engineers who can solve puzzles in isolation. It's about identifying those who will thrive in your specific environment, collaborate effectively with your team, and deliver value to your customers.

That means designing interviews that test for reality, not ritual.

Start with one interview. Make it 10% more realistic. See what changes.

Because somewhere out there is an engineer who would be fantastic on your team but can't remember if MongoDB uses documents or collections in the heat of an interview.

Do you really want to miss out on them because of that?

Implementing an FDE hiring program? See my FDE Advisory Materials for interview templates, scorecards, and detailed process guides.

  • in hiring
  • 5 min read

Context Matters

Hiring is often viewed as a universal process -- "hire the best engineers, obviously!" -- but the reality is far more nuanced.

Hiring is deeply context-dependent, and understanding this can make the difference between building a thriving team and struggling with misaligned talent. As someone who has worked extensively in hiring and team building, I've observed firsthand how crucial it is to align your hiring strategy with your company's current stage, technical needs, and overall vision. This blog post discusses three distinct scenarios I've recently encountered while consulting for different clients, each facing unique hiring challenges.

Through these examples, we'll explore:

  1. How a solo founder can build their founding team
  2. The approach an early-stage startup should take when scaling their engineering team
  3. Why a Series A startup needed to shift focus from engineering to operations

Each case study offers insights into the critical thinking required to design effective hiring processes that go beyond simply filling roles. We'll discuss how to identify the truly important traits for each context, how to redesign job descriptions to attract the right candidates, and how to create interview processes that effectively evaluate the skills and attributes that matter most.

By the end of this post, you'll have a deeper understanding of why one-size-fits-all hiring rarely works, and how to approach hiring strategically based on your company's specific context and needs.

Client 1: A solo founder hiring their founding team

I am working with a product-minded solo founder with a ML background who is looking to hire their founding team. Since they don't have a software engineering background, designing the right hiring process was a crucial problem to solve.

We started by identifying the critical first hire - an engineer who could help drive the technical vision of the company. The job description they had in place had a ton of technical jargon and was focused on the wrong things. The critical insight here was while being pre-pmf, the first couple of engineers need to have spikes in one of the following two areas:

  1. Highly Creative: They need to be someone who can explore the product space and come up with innovative solutions.
  2. Strong Execution: They need to be someone who can take a vague idea and turn it into a product really quickly.

As you are trying to find your PMF, you need to be able to iterate quickly and try out a bunch of different things. This means you need to hire for speed and creativity, not for scale and robustness.

Once we aligned on this, we redesigned the job description to focus on these two areas and started sourcing candidates from our network. We also started working on a technical interview process that would help us identify these traits in candidates using techniques from my previous posts on hiring.

Client 2: An early stage startup looking to scale

This client is an early stage startup with about 10 engineers looking to scale to 20-30 engineers in the next 6 months. They have a couple of products showing early signs of traction and are looking to build out their engineering team. The current team is very product-focused and has a strong engineering culture. They are looking to hire engineers who can come in and start contributing to the product quickly and autonomously. The kind of profiles they are looking for are similar to the Forward Deployed Engineer role at Palantir.

This was obviously an area I have a lot of experience in. The key value add here was helping them design a hiring process that would help them identify engineers who could come in and start contributing quickly. This was done by first shadowing the current interviews and identifying the key areas where they were struggling to evaluate candidates - this could be questions they were asking, or the way they were synthesising the signal from the interviews. I then gave the interviewers feedback on what's working well, and areas where they could improve. I even conducted a few interviews myself to help them calibrate their bar.

Even when you know what you are looking for, it is not always easy to evaluate it in an interview. This is where having an experienced interviewer can make a big difference. During the interview, it is important to be ruthlessly objective about the signal you are getting. This means you form a hypothesis about the candidate, and then try to disprove it with your next question.

Client 3: A series A startup looking to expand their operational team

This was a very different problem to solve. The core technical and operational strategy of the company relied more on the operational team than the engineering team. Despite this their focus was on hiring engineers, and they were struggling to find the right profiles, and to keep them engaged once they joined.

The key insight here was that the operational team was the bottleneck in the company's growth, and they needed to hire for that team first. Once we aligned on this, we started working on a hiring process for the operational team. We also worked on hiring a few key engineers who could help the operational team scale - this had a much wider impact than hiring engineers directly.

It is important to be true to your company's needs and not get caught up in the hype around hiring engineers. Sometimes the most impactful hires are not in the areas you initially think. Having those honest conversations with the founders can make a big difference in the hiring strategy.

Conclusion

Through these engagements I learned a lot about how context matters in hiring. Here are a few key takeaways:

  1. Align with Your Stage: Whether you're pre-PMF, showing early traction, or entering a growth phase, your hiring needs will vary dramatically. Prioritize the skills and attributes that will drive your company forward at its current stage.
  2. Look Beyond Technical Skills: While technical proficiency is important, factors like creativity, execution speed, and cultural fit can be equally, if not more, crucial, especially in early-stage companies.
  3. Adapt Your Process: Your hiring process should reflect your company's needs and culture. Whether it's designing creative technical interviews, focusing on operational skills, or prioritizing autonomous contributors, tailor your approach to find the right fit.
  4. Understand Your Bottlenecks: Sometimes, the most impactful hires aren't in the areas you initially think. Be open to reassessing your needs and focusing on the roles that will truly drive your company's growth.
  5. Continuously Refine: As your company evolves, so too should your hiring strategy. Regularly reassess and adjust your approach to ensure you're attracting and selecting the talent that aligns with your current and future needs.

Effective hiring is not just about filling roles; it's about building a team that can execute on your vision and drive your company forward. By understanding the nuances of your company's context – its stage, culture, and strategic goals – you can design a hiring process that not only attracts top talent but also ensures that talent is the right fit for your specific needs.

Remember, the goal isn't to hire the "best" people in absolute terms, but to hire the right people for your context. This mindset shift can make all the difference in building a team that's truly equipped to tackle your unique challenges and opportunities.

Implementing an FDE hiring program? See my FDE Advisory Materials for interview templates, scorecards, and detailed process guides.

  • in hiring
  • 2 min read

Interviews are Unfair

And that's ok.

As I have mentioned before, genuinely connecting with the candidate makes you an effective interviewer. Having high empathy for the candidate helps build that genuine connection. However, that makes it easy to fall into the "fairness trap".

Fairness Trap

"Fairness trap" is where you, the interviewer, hold yourself to a high standard of fairness. You want to do right by the candidate. It is natural - you have just spent a solid amount of time going through their background, and other interviewers' feedback, and then spent an hour or so interviewing them yourself - it makes sense that you feel a connection. But this connection can cloud your judgment, especially when evaluating candidates who are good, but not great.

Hiring is Existential

For early-stage companies, hiring is existential. Each new team member can significantly impact the company's trajectory. While you might have predetermined criteria for ideal candidates, meeting these baseline requirements isn't always enough.

When evaluating a candidate, consider:

  • What new capabilities will this hire bring to the team?
  • How much potential for growth does the candidate have?
  • What is their "ceiling" - the maximum impact they could have on the company?

These questions help you look beyond surface-level qualifications and consider the candidate's long-term value to your organization.

While data and criteria are important, don't underestimate the value of your intuition. Trust your gut feeling about a candidate, but be aware of your own biases.

Empathy and Objectivity

The key to effective interviewing lies in balancing empathy with objectivity:

  1. During the interview: Build a genuine connection with the candidate. Show empathy and create a comfortable environment for open discussion.
  2. After the interview: Step back and evaluate objectively. Consider the candidate's fit, potential, and possible growth trajectories within your company.

Conclusion

Interviews are inherently unfair because they can never capture a person's full potential or fit within a short interaction. However, by acknowledging this limitation and consciously balancing empathy with objectivity, we can make more informed hiring decisions.

Remember, the goal isn't just to be fair to the candidate in the moment, but to make the best decision for your company's future. Sometimes, that means passing on a good candidate in hopes of finding a great one.

Implementing an FDE hiring program? See my FDE Advisory Materials for interview templates, scorecards, and detailed process guides.

  • in hiring
  • 2 min read

Debugging, Interviewing and Complex Systems

Debugging

Debugging is in its essence diagnosing the behaviours of a complex system. One needs to probe and observe the system to understand why it's doing something it's not supposed to do. Debugging using the scientific approach usually works quite well:

  1. Reason about collected data
  2. Form a hypothesis.
  3. Probe the system to test the hypothesis - this could be in the form of providing a certain input, collecting more data, or altering the underlying infrastructure.
  4. If hypothesis is disproved - repeat 1 & 2.

Interviewing

Interviewing is not that different from debugging in that sense. Your goal is to understand a fairly complex system, to develop a sense of how the system would perform in different situations. Approaching an interview with the same scientific approach can be an effective strategy.

  1. You start with some initial data - this would be the resume, or notes from other interviewers.
  2. Form a hypothesis.
  3. Ask a question that tests the hypothesis.
  4. Go back to 1 and repeat until you are satisfied with your understanding of the system.

For example, if you see a lot of academic projects on the resume, you may form the hypothesis that this is an individual who is academically inclined and might not end up prioritising the customer's needs. You could test that by asking questions like

  • How do you go about prioritising your work?
  • What was the most important aspect of a project that you worked on?
  • Have you ever considered doing a PhD, why/why not?

The answers will prove/disprove your hypothesis, but will also provide you with more information to form new hypotheses.

Conclusion

A lot of engineers do not like interviewing. I find that a bit strange because as engineers we reason about complex systems on a daily basis. Interviewing is not that different. Taking a scientific approach to interviewing is not only effective, but will hopefully help you enjoy the process of interviewing - in turn making you a better interviewer.

Implementing an FDE hiring program? See my FDE Advisory Materials for interview templates, scorecards, and detailed process guides.

  • in hiring
  • 3 min read

Motivational Interview

Why have a motivational interview?

Often times companies will do a "culture fit" interview. This is usually done to understand whether the things that motivate the candidate are aligned with the company values and culture. It is also to see whether they joining the team will have a non-linear effect on the wider team - in addition to their individual performance, do they have a positive effect on the people around them.

However, you often run risk of hiring similar profiles and as a result losing out on a lot of candidates. It is important to be mindful about why you are conducting this interview.

Everyone has a few things that drive them, keep them engaged in their work. The question you are trying to answer is not whether the person is motivated, but what is it that motivates the person and why? Is that something we can give them?

Different types of motivations

Broadly you can classify motivations in two categories: intrinsic and extrinsic.

Intrinsic motivations are based on the individual. These can be both positive and negative.

  • Do they care about learning?
  • Do they care about making their environment better?
  • Are they achievement driven?
  • Are they an adrenaline junkie who thrive in stressful situations?

Then there are extrinsic motivations.

  • Promotion and career advancement
  • Do they expect external fairness, and do something thinking that it's fair?
  • Being part of a group
  • Fear

Notice that neither intrinsic nor extrinsic motivations are necessarily good or bad. It is also important to understand that good motivation does not equate with a good person. Don't hire someone because they are nice and friendly, but hire them because their motivation translates into them being productive.

What to ask?

There are different ways to approach this. A lot of them end up in open ended how questions.

  • How did they decide to change jobs?
  • How did they choose which companies to interview at?
  • If they had multiple offers at some point, how did they choose which one to pick?

You can also get them to open up by asking them to talk about something they are passionate about.

  • Tell me about the last time you heard an interesting problem and were really invested in solving it.
  • Tell me about a passion project of yours. And then ask follow up questions.
  • What was your proudest achievement? What achievement of yours do you think went unnoticed?

Honestly, it is just about being genuinely curious and getting to know them as a person.

Conclusion

Conducting a motivational interview is not just about identifying whether a candidate fits into the existing culture of the company. Instead, it's about understanding the underlying drivers that keep them engaged and productive in their work. If you manage to understand that at a deep level that can guide your hiring as well as staffing decisions.

Remember that a motivational interview should be approached with genuine curiosity. Investigating a candidate's motivations through thoughtful, open-ended questions can reveal insights that go far beyond their resume. This approach ensures that you are not only bringing in capable individuals but also those whose motivations align with and enhance the team's dynamic.

Implementing an FDE hiring program? See my FDE Advisory Materials for interview templates, scorecards, and detailed process guides.