Research Scientist: Post-Training

This role focuses on making large pretrained robot models useful, controllable, safe, and performant in the real world through fine-tuning, reinforcement learning, and on-robot validation. The position involves designing adaptation strategies, improving reliability, and building evaluation frameworks to measure real-world robot performance. It aims to bridge the gap between foundation models and physical deployment by closing the loop between model outputs and physical-world outcomes.