AI Data Annotator
Trained and fine-tuned LLMs using Reinforcement Learning from Human Feedback (RLHF) in partnership with leading AI research labs. Refined model behavior to reduce toxicity, improve factual accuracy, and enhance creative writing quality. Evaluated AI outputs for accuracy, safety, and instruction-following to consistently achieve a rigorous quality score above 95%. • Labeled and assessed AI-generated code and prose for correctness and safety. • Designed and authored red-teaming prompts to probe model limitations. • Identified potential vulnerabilities such as bias and hate speech through adversarial testing. • Measured and reported performance using quality metrics to ensure annotation standards were met.