Lead research on action-conditioned world models and reward models for robot policy evaluation. Train generative and policy models, design rigorous evaluations, validate rewards against human judgments and task outcomes, and ship research into production systems. Collaborate directly with customers deploying robots, while shaping research direction across world models, robot learning, and real-world robotics.

Own the end-to-end robotics software stack, including controls, hardware integration, teleoperation, data collection, testing, deployment, and learned-policy integration. Build data pipelines and infrastructure for training and inference, evaluate world models on real robots, and collaborate with customers deploying robotic systems. This founding role requires broad ownership across robotics, machine learning, software, hardware, and production systems.