Engineering organizations usually notice scaling problems in obvious places first: infrastructure, delivery, communication, coordination. Hiring processes rarely make that list. At least not initially.
For a long time, our hiring process seemed to work reasonably well. Candidates received a take-home assignment, submitted their solution, and we reviewed it internally before moving to follow-up discussions. It was a familiar setup, widely used across the industry, and it produced decent hiring outcomes: hires we felt good about, at a pace that felt manageable.
But over time, we realized we had been too focused on running the process instead of stopping to question it. The problem was the operational cost and fragility of the process itself; the reviewer fatigue, the inconsistency that crept in when people were stretched thin, and the brittleness of a process that depended on too few hands.
What started as a discussion about replacing take-home assignments with live coding eventually became something much bigger:
How do you redesign a process so ownership can scale beyond a small number of people?
This article is not really about live coding versus take-home assignments. It is about simplifying an important engineering process so it becomes teachable, repeatable, scalable, and collectively owned by the team.
What Running the Process Taught Us
We did not sit down one day and decide to redesign our hiring process. The realization came gradually, built up through the friction of running it: patterns we kept noticing, problems we kept deferring, and questions we kept asking ourselves after every hiring cycle. Eventually we had accumulated enough signal that the direction was clear.
The Process We Inherited
When new people became involved in hiring, the onboarding material was essentially the assignment PDF itself. The mechanics were undocumented. The decision-making even more so. The real evaluation criteria lived in the heads of the people already running the interviews, built up through shadowing discussions, reviewing past submissions, and years of accumulated intuition.
That worked while only a few people were deeply involved. But it created a fragile dependency. Understanding what trade-offs mattered, which shortcuts were acceptable, what architectural decisions were red flags, and how to distinguish strong engineering from polished presentation - none of that was captured in any scalable way. What started as an onboarding inconvenience eventually became a structural bottleneck.
The Hidden Cost of the Process
The workflow looked simple on paper: send the assignment, wait for the submission, review the solution, schedule a follow-up, decide. In practice it was significantly more complicated. Solutions required lengthy internal discussions. Different reviewers focused on different aspects. Calibration took time. The process generated substantial asynchronous overhead, and because it depended on a small number of experienced people, progress bottlenecked around availability. We even began using AI to help review submissions, but it reduced the load without changing the underlying problem.
The result was a hiring cycle that could easily stretch close to a month. Internally, it consumed considerable senior engineering bandwidth. Externally, it created a poor candidate experience. Candidates frequently told us the assignment was too large, and many requested extensions just to complete it. Also strong candidates rarely pause their search while waiting for us to finish our internal discussions, and in a competitive hiring environment, long feedback loops mean losing good people before the process is even over.
AI Started Shifting the Ground
Around the same time, another shift was becoming harder to ignore. AI-assisted coding changed the nature of take-home assignments. Not because candidates were doing something wrong, and not because we discourage AI. We actively promote its use. The problem was that the format increasingly optimized for polished output rather than observable thinking. We found ourselves spending more time validating depth of understanding than discussing engineering decisions.
Some submissions looked exceptionally strong, but follow-up conversations revealed significant gaps. In one case, a candidate declined to share their screen while walking us through their solution, making it difficult to assess how familiar they actually were with the code they had submitted. The assignment no longer evaluated engineering capability alone. It now required us to separately evaluate authorship confidence and depth, and the process became heavier precisely when we needed it to become lighter.
We Were Evaluating the Wrong Things
As we re-examined the process, another realization we had been slow to confront emerged. The assignment required substantial boilerplate and project setup work just to bootstrap a solution. Those are genuine engineering skills. But we had to ask ourselves: how representative was this of the work engineers actually do in our organization?
We are a product-oriented company. Our engineers spend most of their time evolving existing systems, reasoning about trade-offs, debugging production issues, and iterating on long-lived codebases. They rarely bootstrap entirely new projects from scratch. Over time we recognized that our interview process had been rewarding greenfield setup efficiency rather than collaborative product engineering.
The Real Bottleneck Was Ownership
At this point, the conversation stopped being about interview formats. The real issue was organizational scalability. Only a small number of people could confidently evaluate submissions. The process depended on context and reviewer experience that were difficult to transfer. Scaling hiring meant scaling dependency on the same individuals.
That led us to a broader realization: complexity naturally concentrates ownership. We were not simply trying to improve interviews. We were trying to distribute decision-making across the engineering organization. But delegation becomes extremely difficult when a process is ambiguous, context-heavy, cognitively expensive, and dependent on intuition that is hard to teach. We could not scale ownership of a process that only a few people knew how to execute consistently. That became the real problem to solve.
From Problem to Process
Once we framed the problem correctly, our goals became much clearer: reduce the hiring cycle from close to a month to a few days or a week, reduce coordination overhead, make interviewer onboarding easier, improve evaluation consistency, and create a process that reflected our actual engineering reality. These were not small adjustments. They pointed toward a fundamentally different kind of process. That is where live coding entered the discussion.
We chose live coding not because it is universally superior or because take-home assignments are inherently flawed, but because it aligned better with the constraints we were trying to solve. The biggest advantage was not speed. It was shared context. Instead of reviewing a polished artifact asynchronously, interviewers and candidates collaborate in real time. We observe problem decomposition, communication, trade-off reasoning, and adaptability. The signal is more immediate and easier to calibrate across interviewers.
Switching to live coding was only part of the work. The other part was building a process rigorous enough to be distributed. We developed a full playbook covering both technical and communication dimensions of evaluation. Assignments come with boilerplate already in place, and we maintain a pool of exercises calibrated to each seniority level. Interviewers work from a structured scorecard, a feedback form helps us measure how the process is performing over time, and guidance is available for anyone running interviews for the first time. The process is aligned with HR so handoffs are clean. What used to live in the heads of a few people is now embedded in the process itself.
The deeper lesson from this experience had very little to do with hiring specifically. Delegation is rarely blocked by willingness. More often it is blocked by ambiguity. Organizations frequently try to distribute ownership without first simplifying the underlying process, then wonder why only a small number of people can operate it effectively. A process becomes scalable when expectations are explicit, onboarding is teachable, and cognitive overhead is reduced. Simplification is not about removing rigor. It is about removing unnecessary operational friction.
The most obvious improvement was speed, but the more important improvement was organizational. Hiring is no longer the responsibility of a small group. It is a capability owned by the team. Eight additional engineers are now actively involved in the process, and that number continues to grow. Even the steering of the process itself has been handed to the team. That is the real outcome we were aiming for.
Trade-offs Still Exist
None of this means live coding is without limitations. Some candidates perform better in asynchronous environments, interview stress is real, and calibration and interviewer training still matter. To ease some of that pressure, we recently started sharing the repository a day in advance. Not the assignment itself, which remains a surprise, but enough context for candidates to familiarize themselves with the codebase before the session begins. We also encounter practical friction that is harder to control. Candidates occasionally ghost scheduled sessions, which introduces its own scheduling overhead. We are still learning and adjusting as we go.
Final Thoughts
This journey started as a discussion about replacing take-home assignments. It ended as a lesson about scalability, delegation, and organizational design. As engineering organizations grow, the challenge is not only building better systems. It is building processes that can be collectively owned. That often requires simplifying things that previously depended on experience, intuition, and hidden context. Because sustainable organizations are not built around a few people carrying critical processes indefinitely. They are built when important work becomes teachable, repeatable, and distributed across the team.