Physical AI Engineering Lead

What we're looking for :

- Strong hands-on foundation in AI/ML and robotics, including modern robot learning approaches, imitation learning, policy learning, or VLA architectures. You can read a policy-learning paper and also debug a TF tree at 11pm.

- Solid grounding in kinematics, motion planning, and control (MoveIt, OMPL, or equivalent), with Python and C++ fluency in a real-time context where you've written code that runs inside a control loop, not just around one.

- Production or research depth in ROS 2, and simulation environments such as Isaac Sim / Isaac Lab, MuJoCo, Gazebo, or Unity.

- Experience building teleoperation and data-collection pipelines, trajectory capture, dataset curation, and the tooling that makes a demonstration corpus usable for training.

- Demonstrated experience taking a robotics or embodied AI system from prototype to something that ran reliably outside the lab.

- Track record of technical leadership: architecture decisions, code review culture, and engineers who got better because they worked for you.

- Comfort in front of customers and executives. You can explain a sim-to-real gap to a plant head and a business case to a CXO in the same afternoon.

Bonus points :

- Master's or Ph.D. in Robotics, AI, ML, or a related field.

- Published work in robot learning, manipulation, or locomotion.

- Familiarity with VLA models (OpenVLA, 0, Gemini Robotics class) or LeRobot.

- Deployment under tight hardware constraints: on-device inference, latency budgets, Jetson-class compute.

- Manipulation-heavy experience: grasping, force control, dexterous end-effectors.

- Background in consulting, product innovation, or enterprise AI delivery where you've shipped under someone else's constraints.