AI Alignment and RLHF Data Labeler/Evaluator
Performed reinforcement learning from human feedback (RLHF) on large language models to enhance alignment and reasoning. Evaluated model outputs for quality, ethical adherence, and logical accuracy using adversarial red teaming and structured prompt evaluation. Delivered high-signal preference data and prompt/response reviews for training autonomous AI agents. • Conducted hands-on prompt processing and red team testing in multi-agent LLM environments. • Rated, evaluated, and provided feedback across a wide range of AI-generated outputs, including technical and cybersecurity content. • Executed persistent, asynchronous model evaluations for context retention and advanced reasoning tasks. • Leveraged specialized mobile-centric infrastructure to assess mobile-first AI agent deployment and security.