Research data collection strategies and design high-impact data slices to reveal model failure modes
Model annotator behavior and design experiments to optimize instruction clarity and reward signals
Develop metrics and frameworks to evaluate dataset quality, diversity, and alignment impact

🎯 Requirements

Deep curiosity about data: how structure, quality, and selection affect LLMs
Bias toward insight and iteration: think critically, move fast, lightweight experiments
Desire to shape AI development: build the foundational data layer for next-gen AI

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