Backend, frontend, full-stack

Hiring software engineering? Here's how we evaluate.

We score how candidates reason about systems and trade-offs — not whether they can recite the right framework. The model conducts the interview; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every software engineering candidate is scored against.

Technical accuracy
Did the answer hold up under probing? We don't reward confident-sounding wrong answers, and we don't penalize a candidate for thinking out loud and changing their mind.
Problem-solving approach
How they decompose a problem, where they place their first hypothesis, what they verify before committing — and whether they recover when a path doesn't work.
Communication clarity
Whether their explanation would survive being repeated by a teammate. Vague hand-waving and jargon as a smokescreen are surfaced explicitly.
Trade-off awareness
Engineers ship constraints, not perfect designs. We score whether the candidate names the trade-offs they're making, or whether they pretend the trade-offs don't exist.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
Walk through a recent production bug.
We probe hypothesis order, what they checked first, how they ruled out causes, and whether they could explain the fix to a non-engineer afterward. The answer that wins isn't the one with the cleverest debugger trick — it's the one with the most disciplined reasoning.
Scenario 2
Design a small system for a stated constraint.
We score how they choose between options, not whether they pick the 'right' tool. A senior candidate naming three reasonable approaches and choosing one with eyes open beats a junior candidate confidently picking the trendy one.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • Time-on-question is tracked relative to a tenant baseline. Sudden, suspicious bursts get surfaced.
  • Admin labeling lets your team flag questions that produced misleading scores; those flags feed back into the calibration loop.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Per-question accuracy score, problem-solving score, and communication-clarity score for every candidate — plus a confidence band on each. We measure how often our scores agree with your hiring decisions over time, and recalibrate when they drift. The number that matters most: the rate at which you trust our 'no hire' signal enough to skip the second-round panel.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live software engineering interview transcript — including how the integrity signals played out — in 15 minutes.

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