How Software Engineering Leaders Make Better Hiring Decisions

The practice that helped most was a short paid working session on a real, small problem from our own backlog. We watch how the person asks questions, how they react when the requirements change midway, and whether they say "I don't know" honestly. Those signals told us more than any whiteboard round.

I once hired someone who interviewed brilliantly and then struggled whenever the spec was vague. Our environment is full of vague specs because the physical world is messy, so it was a bad fit for both sides.

Now I screen for comfort with ambiguity first and raw skill second. Skill can be taught on the job. Judgment under uncertainty is much harder to teach.

1. I always ask one specific question to any candidate: "Tell me about the worst outage you caused". Not one you witnessed, but one you caused. I want to see if engineers who really ran production for you in the past have stories to tell, with timestamps, root cause, fixes and changes made after the fact. Candidates who never broke anything in their lives either never shipped anything or are just not going to take ownership of anything at TradingFXVPS either. Our MT4 and MT5 servers run with live trades on them. A single node degrading at 2am during peak volatility doesn't care about any algorithm puzzles you were given in the interview, and about your ability to reverse a list of nodes in reverse order on a whiteboard. I don't care about any of that. What I care about is how you will act and think during an incident when no one can pick up a call for you. So algorithm puzzles are out, and I only care about something you wrote in the past: a runbook, a postmortem or even a very long explanation of a Slack conversation (with names removed, naturally).

2. Years ago, we brought on board an incredibly talented engineer who listed a big name company on his resume. I had expected him to hit the ground running, given the wealth of experience he had at a company with a well-established SRE team, change board, and three levels of escalation. Our first real failure was a disk filling up on a broker's server at 3am. The engineer opened a ticket and waited. I had hired the logo of a big name company and had no idea whether or not he had worked to resolve problems outside of a safety net of people above him to call. Today, I would ask that candidate "What did you fix most recently when there were no other people above you to go to?". If they cannot give me enough information to answer that question, then the answer is no. I care where someone worked, but that does not tell me how someone will act when the system is down and the room is empty.

Years ago, we relied heavily on standard algorithmic whiteboard problems. We hired candidates who could implement complex binary search algorithms in twenty minutes, but once on the job, they froze when faced with a 150,000-line codebase, unhandled null exceptions, or flaky background jobs. We threw that out. Today, our primary evaluation is handing the candidate a messy 150-line pull request extracted from an actual historic bug in our repository. Candidates who merely 'interview well' complain about the lack of documentation or attempt an unnecessary total rewrite. Candidates who thrive in our high-cadence engineering team immediately inspect the git commit history, identify the edge-case race condition, inquire about the deployment blast radius, and write two clean, hermetic tests that prove the fix before touching production logic. Never test abstract memorization when what you actually need is diagnostic judgment.

We are an agency hiring remote contractors, and our roles are copywriters and a cold email specialist, not engineers. The practice should still carry over: the candidate does the work before anyone talks to them.

The process has five stages. The job post asks applicants to start their email with the line "RMD RMD", which tells us they read the whole post. A short email screening of five questions comes next. Then a first test of 45 to 60 minutes, built from the daily work of the role, unpaid and done without AI tools. Only the top three to five go on to a second test of two to three hours, which is paid. The video call comes after that, and only for the people who passed it. Each test has a written scoring rubric.

What this changes is what we learn first. A conversation tells us how someone talks about the work. A test written the way the job is actually done, remotely and in writing, shows us the work itself. The interview comes last, and by then we are talking to someone whose work we have already read.

I cannot give you a lesson backed by results. We started hiring this way this year and I have no outcome data on the people it selected. What I can say is why the order is what it is: we put the interview at the end instead of the start.

We ask candidates to share examples of work that did not go as planned, then focus on how they handled handoffs. Engineering success depends on more than code alone. It also relies on how people communicate delays, raise risks, and ask for help before small issues grow. These moments reveal how well someone works with others under pressure.

Strong candidates explain these situations clearly, including what they noticed, who needed information, and what they changed afterward. This helps us distinguish real accountability from simply performing well. Even an excellent portfolio may not reflect how someone handles shifting priorities and shared decisions. We value people who communicate openly, raise concerns early, and build trust through teamwork.

The best way to identify strong engineering candidates is to see how they approach real problems, not just how well they answer technical questions. As Director of Technology at 7aSavvy, I'd recommend using practical exercises that reflect the team's daily work. For example, asking a candidate to review a borrower intake workflow, identify potential data validation issues and explain how they'd improve it reveals their problem-solving ability, communication style and understanding of business priorities.

One interview practice I find particularly valuable is asking candidates to explain their decisions and trade-offs during the exercise. Someone might write technically correct code but struggle to explain how it affects users or fits into existing systems. That distinction matters in fintech, where accuracy and reliability are just as important as speed. The lesson is to hire for practical judgment and collaboration, not just technical confidence during an interview.

I watch how a candidate behaves between the interviews, not inside them. After the first conversation I send a small, slightly underspecified task, something real from the backlog, and I pay attention to what comes back before the code does. Some people ask two sharp clarifying questions, and some build for three days on an assumption nobody confirmed. That gap tells me more about whether someone survives a growing team than any whiteboard round, because a scaling engineering org is mostly ambiguity.

I learned this from a candidate who interviewed beautifully. Clean answers, strong fundamentals, every system design question handled. Once on the team, he went quiet for weeks at a time and only surfaced when something was already finished and already wrong.

He wasn't weak. He simply needed more structure than we could give at that stage.

So now I interview for the environment, not the abstract role. If placements are moving in days rather than months, as ours often do, there's no long runway for someone to find their footing. I ask directly about the messiest project they've joined mid-flight and who they went to first when they got stuck. If the answer is nobody, that's my signal, no matter how good the rest of the conversation sounded.

Finding candidates who do well in an expanding tech environment requires looking beyond just the coding speed trap. When scaling a globally distributed team from a few people to hundreds, I saw that a candidate who can solve a complicated programming puzzle in ten minutes may still not be a good match if they are unable to articulate their reasoning to a non-technical stakeholder or to adapt when requirements change. To find those who will be successful, I make sure to focus on their problem-solving process and communication style rather than on strict technical scores.

Early in our growth, I made the mistake of hiring a developer only because of impressive results in technical tests. He was indeed a great coder, but he worked as a lone wolf. When project requirements changed due to the feedback from clients, something that normally happens in software delivery, he reacted defensively instead of collaborating. This lack of active listening, which I believe is the most underrated skill in IT, resulted in a lot of friction and delays in the project's development. That experience showed me that technical brilliance is a prerequisite, not a differentiator. If a candidate is unable to link a business idea to its implementation through communication, his/her code won't be able to help the project.

Now we practice collaborative debugging as a core interview practice. Rather than ask the candidate to write a new piece of code from scratch, we look at an already existing one together. I observe how the candidate works with suggestions, whether he/she asks questions regarding the business context, and how he/she explains the compromises of his/her solutions. A successful candidate is one who considers software to be not just a set of tasks to perform, but a solution to a human issue.

An interview experience taught an important lesson about hiring. Earlier in a career, speed was often valued too highly during interviews. Candidates who answered immediately seemed more decisive than those who took time to think. One hiring decision challenged this belief when a smart and energetic candidate made quick choices without testing the assumptions, forcing the team to revisit avoidable mistakes.

Now, interview questions are designed to reward thoughtful questions before quick answers. The goal is to understand what evidence is missing, who needs to be consulted, and how a decision would be checked. Taking time to think does not mean lacking confidence. In a growing business, careful judgment helps prevent confident mistakes and supports better decisions.

Interview performance can conceal a costly habit, solving the stated problem while leaving the next engineer to rediscover context. Ask candidates to hand off a design decision to a teammate who missed the meeting. Strong responses identify the decision, rejected options, dependencies, and signals that trigger reconsideration. This is practical risk control in fast-moving environments.

A hiring decision taught me that incomplete handoffs can outweigh raw speed. A fast builder joined the team, but decisions lived in messages and memory. I now look for candidates who create context as they work, especially when a choice affects reliability, privacy, or future change. Documentation is not ceremony. It preserves momentum as the team grows.

Give them a small piece of real work and watch what they do with the underspecified part. That tells me more than any whiteboard round. On a small team nobody hands you a ticket with every edge case written out, so I care about what a person does when the spec has a hole in it. Do they ask a sharp question, or make a call and say why?

The practice I trust most is a short paid work sample on a real, bounded problem, followed by a conversation about their choices. For a mobile app that might be one screen that has to survive a slow network and a user who backs out halfway. There's no single right answer. I read what they chose to handle first, what they left out on purpose, and what they'd regret shipping.

The lesson I keep coming back to is that fluent talkers can describe a system they never owned. So I ask about a decision they'd reverse and what broke when they made it. Someone who has shipped gives me specifics, a timeline, and a little embarrassment. Someone who rehearsed gives me the architecture diagram.

I also watch how they take feedback during the sample review. A person who changes their mind mid-conversation for a good reason is usually easy to work next to at 11pm before a release.

For a growing software engineering team, I use a live code review to see how a senior candidate would work with other engineers. I ask them to review a change, identify which comments should block it, cite the evidence for each objection and say how they'd phrase it to the author. Fluency in an interview tells me less about collaboration than a decision on a real piece of work. The candidate has to decide what warrants holding a change and explain that judgment to a teammate.

The practice taught me to probe the gap between a blocking issue and a preference. If someone spots a potential failure, I ask which test would reproduce it or which requirement the change violates. For a naming objection, I ask whether they'd stop the change over it. I listen closely to the comment they'd leave: a specific request gives the author a path to fix the code, while a judgment about the person's competence closes down the exchange. Our code reviewers treat review as a way to teach and pass business-logic knowledge across the team. We critique the code, assume good faith and aim for work that's good enough to move ahead without chasing a theoretical ideal.

Use a change close to the work your team will face, then ask the candidate what evidence would change their mind about a blocking comment. Their reasoning tells you how they handle disagreement under the conditions of the job. The wording they would leave for the author tells you whether that judgment helps the team keep working together.

Behavioral interview questions and follow ups, even for technical candidates. You want to ask software engineers what went wrong in the past, when they noticed the issue, what they did, what the consequences were, if the issue could have been avoided, what they will do differently next time. Very polished answers demonstrate that the candidate had a prepared answer. But projects go wrong, tasks go wrong, and getting deeper into how the candidate reacts at different situations is very helpful. The key to these questions of "what went wrong" is that the candidate might have an answer for them, but as you go deeper and keep asking questions, you get to parts that were not trained. It is hard to trust people who don't have failures or do not take any accountability. When candidates say what was wrong with their decision, we learn more about themselves, and how their behavior will affect them in the position that we are hiring for. If you want to test candidates technically, the main point is asking about possibilities to solve a problem, different alternatives, and trade-offs (pros and cons for each answer). To prevent candidates from preparing specifically for the interview, this is useful in the questions "how would you design a system that does X". Here, we learn about how the candidate thinks and makes decisions. The benefit here is that you understand how the candidates weigh complexity vs. simplification, business vs. technical aspects, cost vs. benefit.

Hi, I'm Frank, founder of Four-Leaf, an AI job search assistant. I look at hiring from the candidate side, and that's a useful vantage point here: the interview rewards a different skill set than the job does.

The people who thrive are the ones who can sit with an ambiguous problem and keep moving. Nothing in a standard interview tests that. The resume has become a weak signal on its own. In our Q2 scan of 37,058 job postings, AI tools appeared in 15% of them, but only 1.28% of the 10,891 AI-tool mentions sat in required-skills sections (https://four-leaf.ai/research/ai-stack-index-2026-q2). A resume full of tool names tells you almost nothing about who can do the work.

The practice I've seen beat a panel of interviews is a small work sample shaped like the real job. One task, a real constraint, and the candidate talking through their thinking as they go. Forty-five minutes of that tells you more than three rounds of polished answers.

The lesson I took from the candidate side is that unpaid work samples quietly filter out the strongest people, the ones already employed who won't spend a weekend on spec. Keep the task short, or pay for the time.

I've built and scaled a data platform team at Softline Solutions that manages GCP infrastructure for 1,500+ client accounts. The biggest hiring lesson I learned is simple: great interviewers and great engineers are two different populations, and the overlap is smaller than you think.

Early on I hired someone who crushed every technical question. Clean code on the whiteboard, strong system design answers, confident delivery. But three weeks in, when a BigQuery pipeline broke at 2 AM and logs pointed in five directions at once, they froze. They wanted a clear problem statement. Production doesn't give you that.

So I changed how we interview. Now I take a real incident from our platform (anonymized, obviously) and hand it to the candidate. Messy logs, incomplete context, a system they've never seen. We debug it together, pair-style. I don't care if they solve it. I care about what questions they ask. Do they check assumptions or just guess? Do they read the error message twice or skip it? Do they say "I don't know this part" and ask, or do they bluff?

One candidate bombed the whiteboard round. Couldn't reverse a linked list, got nervous, asked to skip it. But in the incident review she started reading the logs line by line, asked about our retry policy, spotted a race condition I'd missed in the original incident. I hired her. She's now one of the strongest engineers on the team and she's shipped three major pipeline migrations.

The practice taught me something I keep coming back to: curiosity under pressure is the thing you can't teach. You can teach someone BigQuery or Terraform or Kubernetes. You can't teach someone to stay calm when the dashboard is red and dig into a system they've never touched. And that's what running a 1,500-account data platform actually requires every week.

The interview approach I admire is Elon Musk's practice of going deeper into a difficult problem the candidate says they solved. At the 2017 World Government Summit, he explained: "the people that really solved the problem, they know exactly how they solved it. They know the little details." The lesson I take is to examine the reasoning behind a project. For the engineering environment we are building at Market Brew, I would ask a candidate to walk through one real problem: what they personally decided, which alternatives they considered, what failed, and what evidence convinced them the result worked. My follow-up questions would go beyond the prepared overview into a particular tradeoff or unexpected behavior. That fits our work because agents can produce implementations, but a person still needs to define the intended behavior, review the product decisions and validate the result. A polished demonstration gives me less information than a concrete explanation of how someone recognized and corrected a problem. I would also ask where their contribution ended and a teammate's began. Detailed knowledge should help establish the work they actually owned; it should not automatically be treated as proof they led the entire project. Someone who can explain their decisions, acknowledge uncertainty and give others credit is showing the judgment and accountability I value in an engineering team.

I'm on the operations side rather than engineering, but I run much of our hiring pipeline at Flowmingo, and the lesson that stayed with me applies to any small technical team.

When we scored candidates for an AI-focused growth role, 7 of our top 9 were agency founders, startup CEOs or creators with very large followings. On paper they were the strongest people we found. In practice, almost none of them would have taken a startup salary or been happy working inside someone else's process. Our scoring rewarded "impressive" when what we needed was "likely to do well here", so we rebuilt it. The main bar became proof of something the person had built and shipped themselves, and we started checking early whether the role and the pay made sense for them, before anyone spent an hour in interviews.

If I had to keep a single practice, it would be this: before any live call, candidates record a short answer to a real problem we had that week. You learn a lot in those few minutes. The people who settled in well with us mostly just answered what we asked, said what they'd want to check before doing anything, and didn't mind saying "I'd need more information here." The polished interviewees often handed us a tidy framework that could have been written for any company. Listening to someone work through a messy, specific problem has told us more than any conversation about their past wins.

I’ve seen firsthand that hiring, whether it be for an engineer or an executive, needs to change to meet the moment. Recently, Fiverr posted a role opening and received 500 applications in a single day. Too many candidates took an AI-enabled “spray and pray” approach, and we had no real way to surface the people best suited for the role.

To explore how we should identify top talent in the AI era, our recruiting team went back and studied who at Fiverr had actually thrived in their first three months on the job. We dug into what those people had in common. It often wasn't a degree, title, or years of experience. It was things a resume doesn't ask about. Our best team members are creative, curious, driven, and ready to use AI natively to master their talents.

We now have applicants answer four questions, and that's it: What's unexpected about your experience or skills? What's something you wish you'd made? What have you achieved that you're proud of? What's one thing we need to know about you?

The questions surface what a resume never shows: unique hobbies, side projects, the thing your best friend would say about you when you're not in the room. Not the GPA. Not “detail-oriented team player.” It's the human side, and that’s the one thing AI can't add to an application.If those answers show candidates are a good fit for Fiverr, we match them to a role or build one around them.

We stopped trusting the live interview as the main signal. Every engineer we place first takes a timed assessment, and the part that tells us the most isn't the stack questions: it's a code review task, a real pull request with real problems, graded by a person. Someone who interviews well but reviews code carelessly shows it in ten minutes. The second filter is writing. Our clients work async across time zones, so candidates answer the client's brief in writing and in a two-to-three-minute video. The lesson: the best predictor of how someone works is a small sample of the actual work, judged by someone who does that work.