software dataannotation guidelinesdataset curationinter annotator agreementrlhf preference data
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 fluency with annotation stacks (Label Studio, Argilla, Scale, Surge) plus Python and pandas or SQL work for dedup, PII scrubbing, and stratified sampling of corpora.
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 the exact tooling and scripts used to clean, dedupe, and sample a corpus, with dataset sizes and formats.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they balanced label volume against label quality: gold sets, spot-audit rates, vendor throughput, taxonomy granularity, and when they rewrote guidelines instead of adding annotators.
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, such as narrowing a taxonomy or cutting throughput to lift agreement, with the reasoning behind it.
03
Evaluation factor
Evidence and rigour
25% weight
Test measurement of data quality: Cohen's or Krippendorff's agreement scores, adjudication workflows, error taxonomies, and evidence that a curated set moved downstream model eval metrics.
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
Quotes agreement figures before and after guideline revisions and links dataset changes to measurable eval or benchmark movement.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they briefed annotator pools and pushed back on researchers: guideline docs, calibration sessions, data cards, and handling of ambiguous or unsafe content escalations.
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
Describes running calibration rounds with annotators and negotiating scope with ML researchers, citing the documentation they authored.
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