Product design question

Recruitment at Google costs a billion dollars a year. In our search for false positives, we have had a lot of false negatives. How would you build a product to solve this problem?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product sense applied to an internal hiring pipeline, balancing precision and recall tradeoffs in a screening process.

Clarify where false negatives happen most: resume screen, phone screen, or onsite, since the fix differs by stage.
State the goal: reduce qualified candidates rejected too early without letting weak candidates through, avoiding trading one error for the other.
Focus on the resume and phone-screen stage first, since it is the highest volume and cheapest to iterate on.
Propose structured, skills-based work samples instead of proxy signals like school pedigree that correlate poorly with on-the-job success.
Add a calibration loop: track hires' later performance ratings back to their screening scores to see which signals actually predict success.
Define success as a reduction in false-negative rate, measured against a holdout of previously rejected candidates re-reviewed.

Frames false negatives and false positives as a precision-recall tradeoff, not simply "let more people through."
Proposes work-sample or structured signals over pedigree, a known bias-reducing, more predictive approach.
Builds a feedback loop from hire performance back into screening criteria, closing the measurement gap.
States the cost asymmetry explicitly: a bad hire is expensive but so is losing top talent to competitors.

Proposing to simply lower the bar without addressing precision.
Not defining how false negatives would even be measured, since rejected candidates' outcomes are never observed.
Ignoring stage-specific differences in the hiring funnel.

How would you measure false negatives when you never see what rejected candidates would have done?
What is the cost tradeoff between a false positive and false negative hire?
How would you pilot this without disrupting the whole hiring pipeline?