Data Labeling / RLHF Engineer
Data Labeling and RLHF Engineers manage the human feedback pipelines that shape large language model behaviour, and they must communicate guidelines, quality metrics, and disagreement resolution strategies in English to distributed annotation teams. This path covers inter-annotator agreement statistics, preference data schemas, reward model training vocabulary, and the language of labelling guideline documents.
Topics covered
- Annotation Quality Metrics
- RLHF Pipeline Vocabulary
- Preference Data Collection
- Labelling Guideline Writing
- Statistical Agreement
- Reward Model Language
Vocabulary spotlight
4 terms every Data Labeling / RLHF Engineer should know in English:
A statistical measure of the degree to which independent annotators assign the same label to the same data item
"We achieved an inter-annotator agreement of 0.74 Cohen's kappa on the helpfulness dimension after two rounds of calibration."
A training example consisting of two model responses to the same prompt, labelled with a human judgement of which is more desirable
"The reward model was trained on 120,000 preference pairs collected from domain-expert annotators over six weeks."
The process of aligning annotators' judgements through shared examples and discussion before production labelling begins
"After the first calibration session, the team's average agreement on safety ratings improved from 0.61 to 0.82 kappa."
A written document specifying the rules, definitions, and decision trees that annotators must follow when labelling data
"The annotation guideline was updated to clarify the distinction between "unhelpful" and "harmful" responses, which had caused significant annotator confusion."
📚 Vocabulary Reference
Key terms organised by category for Data Labeling / RLHF Engineers:
Annotation Quality
RLHF Pipeline
Labelling Operations
Data Quality
Recommended exercises
Real-world scenarios you'll practise
- Writing an annotation guideline section for a new "factual accuracy" dimension, including worked examples of borderline cases.
- Presenting a calibration report to the ML research team, explaining why kappa dropped below the 0.70 threshold for one task type.
- Drafting an email to a labelling vendor requesting a root-cause analysis for a spike in disagreement rates on the previous week's batch.
- Reviewing a proposed change to the preference data schema and writing feedback on how it will affect downstream reward model training.