Hire data annotators the way you'd hire for quality-critical operations work: pilot with a small paid task, measure inter-annotator agreement, and pay for judgment, because label quality directly caps model and eval quality. The role has stratified, from bulk labeling to expert AI trainers who rate reasoning in specialized domains, and each tier needs a different hiring approach.
The three tiers of the role
- Bulk annotation: high-volume, well-specified labels (categories, bounding boxes, spans). Optimize for consistency and throughput.
- Judgment rating: comparing outputs, rating helpfulness and safety, writing critiques. Optimize for calibrated judgment and written reasoning.
- Expert AI training: domain specialists (medical, legal, code) creating gold answers and grading model reasoning. Optimize for verifiable expertise, pay accordingly.
How to hire them
- 1Write the guideline doc first, if you can't specify the task, you can't judge candidates.
- 2Run a paid pilot task with pre-labeled gold examples mixed in.
- 3Score agreement against gold and against other candidates; hire the consistent ones.
- 4For expert tiers, verify credentials and test on genuinely hard cases.
- 5Onboard with calibration sessions where disagreements get discussed, not averaged away.
Keeping quality high
- Seed every batch with gold examples to catch drift.
- Track per-annotator agreement over time; retrain or rotate outliers.
- Treat guideline updates as versioned releases, relabel when definitions change.
- Pay per hour with quality bonuses, per-item rates alone incentivize speed over care.
