ML, Behavior Learning

You will own how our robots learn new behaviors. That means both the models themselves and the pipeline that produces them: a repeatable path from demonstrations to a policy that works reliably in environments we have never seen.

This is a role for someone who wants their research to run on hardware in the field rather than on a benchmark, and who is early enough in a team's life to set its technical direction.

• Train and ship manipulation policies for production robots.
• Own the behavior acquisition pipeline end to end: demonstrations in, reliable policy out, with a measurable cost per new behavior.
• Make policies work under contact, where the robot applies and reacts to force, and where locomotion and manipulation cannot be treated separately.
• Drive architecture decisions on imitation learning, pretrained representations, and how much to unify across tasks, under a hard constraint that policies run on-device.