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 command of both sides: Meta Model and Milton Model language patterns, plus practical tooling such as prompt templating, Hugging Face datasets, Label Studio or an internal RLHF annotation stack.
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 linguistic patterns (presuppositions, embedded commands, reframes) and shows how each was encoded into prompts, rubrics or labelled turns.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they balance rapport-building conversational style against safety guardrails, hallucination risk and model latency, and where they chose to constrain the assistant rather than coach it.
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 a concrete trade-off, for example dropping a persuasion pattern after it produced manipulative or unsafe completions in evaluation.
03
Evaluation factor
Evidence and rigour
25% weight
Test measurement habits: inter-annotator agreement, rubric calibration rounds, A/B preference tests, win rates against a baseline checkpoint, and how they detected drift in tone quality.
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
Cites kappa or agreement figures, calibration cadence, and a before-and-after preference score tied to a specific dataset revision.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they brief external annotators and hand off to ML engineers: written guidelines, edge-case appendices, disagreement adjudication, and pushback on ambiguous taxonomy definitions.
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
Produced annotation guidelines others reused, ran adjudication sessions, and translated coaching language concepts for engineers without jargon.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
