Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Check hands-on depth with model evaluation harnesses: pytest suites around inference APIs, LLM eval frameworks (DeepEval, Ragas, LangSmith), golden datasets, and CI gating on accuracy or latency thresholds.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names specific eval frameworks and test layers, writes assertions for non-deterministic outputs, and gates releases on measured regression thresholds.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they decide what to test when outputs are probabilistic: sampling strategy, hallucination and toxicity checks, drift monitoring, cost versus coverage on large eval runs.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Explains trade-offs between human review, LLM-as-judge, and rule-based checks, with reasoning about false positives and eval cost.
03
Evaluation factor
Evidence and rigour
25% weight
Assess statistical honesty: how they size eval sets, handle judge disagreement, compute inter-annotator agreement, and separate real model regressions from prompt or data noise.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Quotes concrete metrics (pass rates, precision on flagged cases, confidence intervals) and admits where a result was inconclusive.
04
Evaluation factor
Collaboration and communication
15% weight
Look for evidence of pushing back on shipping a model: bug reports filed against ML engineers, annotation guidelines written, and release sign-off conversations with product owners.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes a specific launch they delayed or amended, with the evidence presented and how the ML team responded.
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