AI Training / Biology Annotation & Review (Remote) — DataAnnotation
TextEvaluated AI model outputs for factual accuracy across complex biology and biomedical topics, using provided written guidelines and rubrics. Assessed relevance, completeness, and correctness while documenting clear justifications for each judgment. Flagged inaccurate, low-quality, or misleading responses while maintaining consistent guideline adherence across tasks.• Reviewed LLM responses covering bioinformatics, computational biology, immunology, and cancer biology. • Applied rubric-driven scoring to judge correctness and alignment with instructions. • Produced structured feedback describing why responses did or did not meet standards. • Handled edge cases by applying rubric criteria consistently when information was ambiguous or incomplete.