software dataai red teamingfairness testinginterpretabilitymodel evaluation
Complete evaluation framework
What to assess and how to score it
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 in evaluation and safety tooling: adversarial robustness libraries, fairness metrics such as equalised odds, interpretability methods (SHAP, integrated gradients), and eval harness code they wrote.
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 metrics, attack methods and libraries used, and explains why each was chosen over cheaper alternatives for that model.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe trade-offs they negotiated: accuracy lost to a fairness constraint, latency added by guardrails, false refusal rates, and how they set thresholds with product owners.
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
Quantifies both sides of a real trade-off and describes the threshold decision, its owner, and the monitoring that followed deployment.
03
Evaluation factor
Evidence and rigour
25% weight
Test rigour in measurement: dataset construction for red-team suites, statistical significance on eval results, drift monitoring, model cards, and alignment with the EU AI Act or NIST AI RMF.
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
Distinguishes signal from noise in eval scores, cites sample sizes or confidence intervals, and ties documentation to a named framework.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they raised uncomfortable findings: a model they recommended blocking, disagreement with a research lead, or writing risk assessments that legal and product both used.
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
Recounts a specific escalation with named counterparts, the evidence presented, and whether the launch was delayed, gated or shipped.
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