AI Trainer — Atlas (Remote)
Trained and evaluated LLM outputs for accuracy, clarity, and policy alignment. Delivered structured RLHF-style feedback to support model improvement. Ran prompt experiments to reduce failure modes and improve instruction-following behavior while maintaining annotation quality through edge-case documentation and checklists. • Assessed LLM responses for quality, correctness, and policy compliance. • Provided structured RLHF-style feedback to guide model updates. • Conducted prompt testing to improve instruction-following and reduce errors. • Used edge-case documentation and checklists to ensure consistent labeling quality.