Your hiring process is a lot like a codebase. When the inputs and outputs are clear, you get reliable results. When things are vague, you start getting mysterious bugs, noisy signals, and the hiring equivalent of undefined.
If you’ve ever looked at your pipeline and thought, “We interviewed ten engineers and I still don’t know who to hire,” that’s exactly what’s happening. The process is returning no clear value.
The good news: you can debug this in the same way a senior engineer debugs an unreliable system – by making the contracts clear and the signals meaningful.
Spotting the “undefined” in your hiring pipeline
There are a few signs your process is returning undefined instead of strong, confident decisions:
- Feedback is vague. Things like “seems smart,” “not a culture fit,” or “I liked them” show up in scorecards, but no one can say why.
- Final decisions feel like a coin toss. Two candidates both “seem good,” but no one can explain the difference in terms that matter to the role.
- Every interviewer uses a different yardstick. One person cares about system design, another obsesses over syntax, and a third only notices personality.
- Post-hire surprises. Six weeks later, you realise the new hire can’t actually do the core parts of the job.
That’s what happens when you don’t define the return type of your hiring process. Everyone is passing around impressions instead of evidence.
Start with a clear function signature for the role
Instead of a fluffy job description, think in terms of a function signature:
SeniorBackendEngineer(candidate) => { canShipFeatureX, canOwnSystemY, canWorkWithTeamZ }
Ask yourself:
- What 3 – 5 concrete outcomes must this person deliver in the first 6 – 12 months?
- What skills or behaviours make those outcomes possible?
- What non-negotiables really matter (and what’s just “nice to have”)?
Examples of clear outcomes:
- “Own our billing service and reduce payment failures by 50%.”
- “Ship at least one meaningful production change per week after onboarding.”
- “Collaborate closely with product and support to debug complex user issues.”
Once those are defined, everything else should tie back to them: your screening, interviews, and final decision. If something in your process doesn’t help you answer, “Can they achieve these outcomes?” it’s probably noise.
Turn fuzzy impressions into concrete signals
Most hiring loops fail not because they lack stages, but because each stage returns fuzzy impressions instead of clear signals. To fix that, give every interview a very specific purpose.
For each stage, define:
- One or two skills this interview must assess (no more).
- What “good” looks like in observable terms.
- What evidence the interviewer should capture.
For example, instead of a generic “technical interview”, you might have:
- Implementation interview: Can the candidate write clear, maintainable code to solve a realistic problem?
- Systems interview: Can they reason about trade-offs in architecture relevant to your stack?
- Collaboration interview: Can they explain decisions, handle disagreement, and understand constraints from non-engineers?
Give interviewers a short checklist or rubric, not a script. The goal is to force them to move from “I liked them” to “They identified the core bottleneck in the scenario, offered two alternatives, and could explain trade-offs without getting defensive.”
Use realistic tasks instead of abstract puzzles
One common source of undefined signals is using tasks that have nothing to do with the real job: leetcode-style puzzles, brainteasers, or toy problems that no one in your company ever solves.
Engineers know when they’re being tested on irrelevant trivia, and strong people will opt out. Worse, you end up optimising for candidates who are good at interviews, not the actual work.
Better options:
- Small, scoped take-home that mirrors your real work. For example, “Here’s a simplified API. Extend it to support feature X and write a short note explaining your choices.”
- Guided pairing session on a realistic problem. Let them navigate a small code sample, ask questions, and iterate with you.
- Architecture discussion built around a real bottleneck you’ve faced (sanitised, of course).
The key is to design tasks where success or failure maps directly to your defined outcomes. If the task doesn’t tell you whether the person can ship in your environment, it’s just noise.
Make feedback structured, not scripted
You don’t need a long, bureaucratic process to improve feedback. You just need a small amount of structure that forces clarity.
After each interview, ask interviewers to answer these in writing:
- What did you test? (One or two skills, max.)
- What evidence did you see? (Specific behaviours, decisions, or questions.)
- How does this map to the role’s outcomes? (Clear connection, or none.)
- Strong yes / weak yes / weak no / strong no – why?
Ban vague terms like “rockstar,” “brilliant,” or “bad vibe” unless they’re backed by examples. For instance:
- Instead of “weak communication,” say “struggled to explain trade-offs without diving into irrelevant details; product partner would likely be lost.”
- Instead of “very smart,” say “identified an edge case we hadn’t mentioned and proposed a simple way to handle it.”
Over time, this builds a feedback library you can review when you notice patterns – both in successful hires and in mistakes.
Close the loop between hiring and reality
Your hiring process only improves if you treat every hire as a test of the system.
Three to six months after someone joins, take one hour to review:
- What is this engineer actually great at?
- Where are they struggling?
- What did we correctly or incorrectly predict during interviews?
- Which parts of our process gave useful signals?
If your highest-performing engineers all “barely passed” some stage, maybe that stage is broken. If weak performers all “crushed” a certain interview, maybe you’re testing the wrong thing there.
Treat the process as living code. Refactor ruthlessly when something isn’t working.
Being explicit attracts better candidates
There’s another benefit to replacing undefined with clarity: strong engineers actually like this.
When your job ad, interview stages, and feedback all line up around clear outcomes, candidates can tell you’re serious. They know what’s expected, what you value, and how they’ll be evaluated. That transparency is rare, and it’s attractive to people who want to do good work, not just win at interviewing.
It also helps you move faster. When everyone knows what good looks like, you don’t need five extra conversations to convince the team. Decisions become quicker and more confident, which means you lose fewer candidates to slow processes.
In the end, hiring doesn’t need to be mysterious. Define what you’re hiring for, design interviews that map to that, and demand evidence instead of impressions. Do that, and your process will stop returning undefined – and start returning engineers who actually move the product forward.
If you want to meet more developers who care about meaningful work, clear expectations, and thoughtful hiring, unicorn.io is built for exactly that. Signing up is free, and it helps you discover engineers – and roles – where the signals are strong on both sides.
