A technical interview should show how someone thinks, builds and works with others. Too many still test how well they perform under artificial pressure.
Technical hiring is one of the hardest parts of scaling a startup.
The stakes are high. A strong engineer can lift the whole team. A weak hire can slow delivery, add complexity and drain the time of people who are already stretched.
So it makes sense that founders and engineering managers want proof.
The problem is that many technical interviews still test the wrong things.
Candidates are asked to solve abstract coding puzzles, complete long take-home tasks or perform live in conditions that look nothing like the work they would actually do.
Some candidates play along. Many good ones do not.
When the assessment feels disconnected from the job, strong candidates start questioning the company’s judgement before the company has finished assessing theirs.
The problem with puzzle-based assessment
Algorithm-heavy tests can be useful in specific contexts. But for many startup roles, they are a poor proxy for success.
Most startup engineering work is not about solving isolated puzzles from memory. It is about making trade-offs, understanding messy requirements, improving existing systems and collaborating with people who do not always speak in technical terms.
A developer may be excellent at the real work but unimpressive in a contrived test. Another may be strong at rehearsed interview problems but weak at maintainable code, product judgement or collaboration.
That creates a hiring risk.
The interview process appears rigorous, but the evidence may be thin.
Weak technical assessment usually shows up in 3 ways:
- the test does not reflect the actual work;
- interviewers assess different things without realising it;
- candidate comparison depends too much on confidence, speed or performance style.
That is not a strong basis for a hiring decision.
If your technical interview rewards interview practice more than job-relevant thinking, it is creating noise.
Real work creates better evidence
Startups need technical interviews that create useful evidence.
That does not mean making the process longer. It means making it more relevant.
A stronger technical interview might involve:
- reviewing a small, realistic pull request;
- debugging a simplified issue from a real product context;
- walking through trade-offs in a system design decision;
- pairing on a contained task with clear constraints;
- discussing how the candidate would improve an existing codebase;
- asking how they would communicate technical risk to a non-technical stakeholder.
These formats do more than test syntax. They show how the candidate reasons.
They also create better interview evidence because the hiring team can observe specific behaviours: how the candidate asks questions, explains trade-offs, responds to feedback and handles uncertainty.
For a startup, that often matters more than whether someone can solve a puzzle quickly while being watched.
Candidate time is part of the assessment
Candidate experience matters in technical hiring because strong engineers usually have options.
If the process feels careless, excessive or disconnected from the role, they may leave before the company has made a decision.
This is especially true for take-home tasks.
A short, focused task can be useful. A vague project that eats a weekend is not respectful. It also gives an advantage to candidates with more spare time, not necessarily candidates with stronger ability.
A better technical task should be:
- Clearly scoped. The expected time commitment is stated and realistic.
- Job-relevant. The task reflects the kind of judgement needed in the role.
- Reviewed consistently. Every candidate is assessed against the same criteria.
- Discussed afterwards. The debrief matters as much as the output.
- Respectful of effort. The process should not feel like unpaid product work.
The best signal often comes from the follow-up conversation. Why did they make that choice? What would they improve with more time? What trade-off did they reject? How would they explain the solution to someone else?
That is where technical judgement becomes visible.
The AI question is now part of the interview
Many developers now use AI-supported coding tools as part of their normal workflow.
Pretending this is not happening makes technical interviews less realistic.
The answer is not to hand the interview over to AI. It is to assess how candidates use modern tools with judgement.
A practical interview might ask a candidate to review AI-generated code, find weaknesses, improve the structure or explain where they would not trust the output.
That can reveal useful evidence:
- Can they spot fragile logic?
- Can they explain trade-offs clearly?
- Can they improve code rather than simply accept it?
- Can they use AI support without outsourcing judgement?
- Can they communicate risk to the wider team?
This keeps the interview grounded in the real world while keeping human judgement central.
The question is no longer whether candidates use AI support. The better question is whether they can use it responsibly and still think for themselves.
Build a technical interview playbook
Many technical interviews are inconsistent because managers default to the way they were interviewed.
That is understandable, but risky.
A growing engineering team needs a shared interview playbook. Not a rigid script, but enough structure to make assessment fairer and easier to compare.
A useful playbook should define:
- which competencies each stage is testing;
- which questions or tasks are used for each role type;
- what good evidence looks like;
- how interviewers should score or summarise feedback;
- what counts as a concern;
- how Impact & contribution should be assessed alongside technical skill;
- what feedback candidates should receive after the process.
This is where structured interviews help. They give teams a way to assess candidates against role-relevant criteria without making the conversation feel robotic.
Technical hiring should be structured enough to compare candidates fairly, but human enough to understand how they actually work.
Where Maslow fits
At Maslow, we are building the Interview Operating System for structured interviews, clearer interview evidence and better-informed hiring decisions.
For technical hiring, the point is not to replace engineering judgement. It is to help teams prepare better interviews, capture stronger evidence and avoid losing useful signal in inconsistent conversations.
A better technical interview does not need to be colder, longer or more complicated.
It needs to be more connected to the work, clearer about what is being assessed and more respectful of the candidate’s time.
Further reading
For a longer version of this article, read Maslow’s full piece on why startup technical interviews are broken.
Build technical interviews that produce better evidence
Maslow is currently opening early access for startups and hiring teams that want to run more structured interviews, capture clearer evidence and improve hiring confidence while keeping human judgement central.
The best technical interviews do not try to catch candidates out. They help both sides understand whether the work, the role and the way of thinking genuinely line up.