Human Feedback & Annotation Specialist (Contract Work)
Annotated conversational datasets by labeling user intent and conversational attributes to support training and evaluation. Ranked competing responses, identified hallucinations, flagged safety issues, and produced gold-standard examples for training. Ensured labeled outputs were consistent with rubric expectations and corrected model behavior through structured feedback. • Labeled intents and conversational categories from dialogue data • Ranked multiple model responses and justified preferences • Flagged hallucinations and safety/policy issues in generated outputs • Created and curated gold-standard training examples