Lead development and deployment of state-of-the-art RL algorithms for locomotion and manipulation on humanoid robots. Prototype in simulation, transfer and fine-tune on hardware, optimize distributed training pipelines, mentor junior engineers, and collaborate with hardware teams to analyze results and improve sim-to-real performance.

Develop and deploy state-of-the-art reinforcement learning algorithms for dynamic locomotion and manipulation on humanoid robots. Drive simulation-to-hardware transfer, build and optimize distributed training pipelines, mentor junior engineers, collaborate with hardware teams to debug system-level issues, and convert human demonstration data into robust reference trajectories.