What it tests

Whether the candidate understands ML system fundamentals well enough to identify problems — and whether they can translate those findings into a clear, credible briefing for a non-technical business audience.

Format

1Stage 1 (45 min, take-home or live): Candidate analyzes an ML deployment scenario — e.g., evaluate a RAG pipeline with inconsistent recall, or review an eval framework with suspected label noise. They document findings: root causes, severity, recommended fixes.
2Stage 2 (30 min): Candidate presents findings to a panel that includes one technical evaluator and one deliberately non-technical stakeholder (a business or ops role). The non-technical evaluator can ask any question.
3Debrief (15 min): Candidate and interviewers discuss the actual root cause and what a production remediation plan would look like.

What to look for

ML diagnosis accuracy — did they identify real issues versus noise in the scenario?
Translation quality — did the non-technical presentation land without dumbing down the substance?
Stakeholder composure — how did they handle questions from someone without technical background, especially challenging or off-topic ones?
Remediation thinking — do they propose fixes that are practical given real production constraints?

Adaptation guide

The ML scenario can be replaced with any technical audit relevant to your product — data quality issues, integration failures, infrastructure bottlenecks. The two-audience presentation format (one technical, one not) is the core of what makes this assessment distinctive. Run it that way regardless of the domain.

Full description

Format:

Stage 1 (45 min): Candidate analyzes a real ML or technical deployment scenario — identifies root causes, severity, and recommended fixes
Stage 2 (30 min): Candidate presents findings to a mixed panel: one technical evaluator and one non-technical business stakeholder who can ask any question
Debrief (15 min): Discussion of the actual root cause and what a production remediation plan would require

Time: 90 minutes total across two stages

What to look for:

ML diagnosis accuracy — real issues identified, not just surface-level observations
Translation quality — non-technical presentation clear without losing substance
Stakeholder composure — handles questions from non-technical audience without condescension or confusion
Remediation thinking — proposed fixes are practical given real constraints

Adaptation: Replace the ML scenario with any technical audit in your product's domain. Keep the two-audience presentation format regardless of domain — it's the signal generator that makes this assessment work.