Practise answering 5 interview questions for Cross-Model Eval Harness Engineer roles. Covers fair multi-provider evaluation design, diagnosing eval-versus-production divergence, preventing scoring exploitation, and communicating leaderboard limitations to leadership.
0 / 5 completed
1 / 5
The interviewer asks: "Why is it hard to compare two different LLM providers fairly using the same evaluation harness?" Which answer shows the deepest technical understanding?
Option B identifies the real confound (prompt-format sensitivity varying by model, not just capability) and proposes a concrete methodology to control for it — equivalent-effort prompt adaptation, standardized sampling, variance-aware sample sizes — while explicitly naming the failure mode of naive identical-prompt comparisons. Option D asserts fairness from an assumption (identical prompt) that option B specifically shows is insufficient. Option C abandons rigor entirely. Option A is the naive approach the question is testing whether the candidate recognizes as flawed.
2 / 5
The interviewer asks: "Your harness shows Model X winning on your eval suite, but a downstream team reports Model Y performs better in their actual application. How do you respond?" Which answer shows the most rigorous investigative process?
Option B investigates methodically — gathering concrete failure cases, checking distributional match between the eval suite and the real application, and checking whether the scoring rubric measures what actually matters downstream — and proposes a constructive resolution (extend coverage or scope claims honestly) rather than dismissing either signal. Option A dismisses valuable real-world evidence out of overconfidence in the harness. Option C blames the other team without evidence. Option D abandons the harness's value proposition instead of investigating and improving it.
3 / 5
The interviewer asks: "How would you design an eval harness to avoid a model 'gaming' the scoring method rather than genuinely performing better?" Which answer is most technically thorough?
Option B gives a genuinely thorough, multi-layered defense: auditing judge bias against human ground truth, triangulating with structurally different scoring signals, refreshing eval cases to prevent implicit overfitting, and proactively red-teaming the rubric itself for exploitability. Option D dismisses a well-documented real risk (reward hacking in LLM-judge evals is a known, common failure mode). Option C oversimplifies to "humans cannot be gamed," which is also not fully true (human raters have their own biases, e.g., favouring confident tone) and is impractical at scale. Option A is directionally right but far less complete than B's systematic approach.
4 / 5
The interviewer asks: "How do you explain the limitations of your eval harness to leadership so they do not over-trust a single leaderboard number?" Which answer communicates this most effectively?
Option B proactively communicates scope, confidence, and the specific things the leaderboard does not measure, and reframes the correct role of the eval score as one structured input rather than a final answer — this is exactly the communication discipline that prevents over-trust. Option D withholds useful information under the assumption stakeholders will not ask the right follow-up questions, which is a passive failure mode. Option C avoids quantitative communication entirely, discarding real value the harness provides. Option A actively encourages the over-trust the question specifically asks how to prevent.
5 / 5
The interviewer asks: "Tell me about a time your eval harness gave a confidently wrong signal about which model to use, and how you caught it." Which answer best demonstrates ownership and technical depth?
Option B is a complete, specific story: a concrete false signal (Model A winning on pass rate), a rigorous verification action (manual review revealing test-fixture overfitting versus genuine correctness), a corrective fix (separate code-quality rubric dimension), and a consequential result (the standardization decision reversed, plus a permanent process improvement). Options C and D avoid demonstrating real experience or specific technical judgment. Option A is vague and lacks the diagnostic detail that makes the story credible and demonstrates genuine expertise.