The live coding interview has become a standard fixture in software hiring. It has also become one of the least useful filters in the process, and most teams administering them have not stopped to ask why they work the way they do.
What Live Coding Actually Measures
Software development is fundamentally a research activity. Engineers spend a significant portion of their working day reading documentation, studying how existing systems behave, cross-referencing library APIs, and iterating on solutions with the full support of their toolchain. Good engineering judgment develops through that kind of deliberate, unhurried problem-solving.
A live coding interview inverts almost every one of those conditions. The candidate is asked to produce correct output under observation, against a clock, from memory, without the context of a real system. What gets measured in that environment is not how someone thinks through a technical problem. It is how someone performs a specific kind of test that bears almost no resemblance to the work they would actually be doing.
The qualities that distinguish strong engineers — the ability to reason about system design, to ask the right clarifying questions, to understand tradeoffs, to communicate decisions, and to incorporate feedback — are largely undetectable in a timed coding exercise.
The Misread Signal
Hiring teams tend to assume that candidates who score well on algorithmic assessments are the strongest engineers. The actual correlation is different. Consistent high performance on LeetCode-style problems reflects sustained, deliberate practice of that specific format. The candidates with the most practice hours are often those with significant gaps in their recent employment history.
This is not a cynical observation. It is a structural one. A working engineer spending fifty or sixty hours a week on production code, incident response, code review, and architectural discussions does not have time to grind interview prep. The candidate who has optimized heavily for the format may be signaling something quite different from engineering excellence, and treating those two things as equivalent produces predictable hiring errors.
A Better Assessment Model
The alternative is to make the assessment resemble the actual job as closely as possible.
Select a real, bounded task from your product backlog — something that requires genuine understanding of the problem domain but can be completed independently without deep institutional knowledge. Share the relevant context with the candidate and ask them to produce a pull request. Review their submission the same way you would review a PR from a team member: look at how they scoped the solution, what they documented, what tradeoffs they acknowledged, and how they engage with feedback.
This approach addresses most of the standard objections. The cheating concern is minimal when the task is genuinely specific to your product. The concern about unpaid labor has a simple answer: compensate candidates for completed submissions. The fee is small relative to the cost of a mis-hire, and it communicates clearly that you value their time.
What This Means for Early-Career Engineers
If you are a student or a new graduate trying to build your profile, the most durable investment is a track record of real work. Open-source contributions, demonstrable side projects, and in particular, experience working on production systems where your PRs are visible, carry substantially more weight in a rigorous hiring process than a high LeetCode rank.
One of the most direct paths to that kind of demonstrated experience is an internship that puts you on real infrastructure alongside working engineers. Rather than treating internships as a resume line, look for programs that give you actual production responsibility. If you are a third or fourth-year engineering student looking for that kind of environment, read more about how our internship opportunities work.
The students who come out of genuine production experience have something they can discuss concretely in any technical interview, and something they can point to when the hiring process is done well.
Where the Industry Needs to Move
The live coding interview persists because it is standardized and administratively simple. It produces a score. Scores feel objective even when the thing being measured has limited relevance to job performance.
A task-based process requires more coordination, more judgment in evaluation, and a willingness to compensate candidates fairly. Those are genuine costs. But the alternative is a process that systematically filters for test-taking aptitude rather than engineering ability, and that is a more expensive mistake to make.
Teams that move toward work-sample evaluation will hire more accurately, lose fewer strong candidates to process friction, and build engineering cultures where the standard for quality is evident from the first interaction.
What We’re Seeing at BootstrapVC
Across our portfolio, the companies building the strongest engineering teams are the ones that have redesigned their hiring around demonstrated work rather than interview theater. The process takes more intentionality to run, but the signal quality is substantially higher and the retention reflects it.
We spend a lot of time thinking about this at the early stage because the hiring practices a company establishes in its first year tend to persist. If you are a founder working through how to build a technical hiring process, or an engineer looking for your first serious production role, we would like to talk. Reach out to our team.
Hire for the work, not the performance of the work.