Interview scorecard template

Trustworthy AI Engineer interview scorecard

Evaluate Trustworthy AI Engineer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so 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. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate names specific metrics, attack methods and libraries used, and explains why each was chosen over cheaper alternatives for that model. For systems and trade-offs, look for evidence the candidate quantifies both sides of a real trade-off and describes the threshold decision, its owner, and the monitoring that followed deployment. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
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.

Evidence-led prompts

Interview questions for a Trustworthy AI Engineer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Have you worked on a project where ethical outcomes were the primary aim?

  2. 02

    Can you describe a time you implemented fairness measures in a project?

  3. 03

    Which programming languages and frameworks do you use for this work?

  4. 04

    What methods do you use to identify and address bias in models?

  5. 05

    Can you describe adjusting a model or dataset to reduce unfairness?

See the complete Trustworthy AI Engineer question set
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