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
Probe hands-on depth with transformer fine-tuning (LoRA, QLoRA), embedding stores like pgvector or Pinecone, PyTorch training loops, and orchestration via LangGraph or bare API calls.
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 models, tokenizer quirks, quantisation choices and inference costs, and explains why they picked each over alternatives.
02
Evaluation factor
Systems and trade-offs
25% weight
Test how they weighed RAG versus fine-tuning versus prompt engineering, chose context window sizes, handled latency budgets, GPU spend, and fallback behaviour when a model provider degrades.
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
Describes concrete trade-offs with numbers: p95 latency, cost per thousand calls, accuracy delta, and the reasoning behind the final architecture.
03
Evaluation factor
Evidence and rigour
25% weight
Assess how they measured model quality: eval sets, LLM-as-judge pipelines, hallucination and retrieval recall metrics, regression suites, offline versus production A/B results.
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
Built a real eval harness before shipping, cites baseline versus improved scores, and admits where metrics failed to capture user harm.
04
Evaluation factor
Collaboration and communication
15% weight
Look for work with product managers, domain experts labelling data, and platform teams; ask how they explained model limitations and non-determinism to non-technical stakeholders.
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
Gives examples of resetting expectations on accuracy, running labelling sessions with domain experts, and documenting known failure modes clearly.
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