Designs and runs ML experiments to evaluate how AI models perform in the real world using human preference data — training reward/preference models, fine-tuning LLMs (RLHF, DPO), and building evaluation metrics that power Arena's public leaderboard. Core stack: Python, PyTorch/JAX/TensorFlow, statistics, and large-scale deep learning research.
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