Robot Learning Engineer — Planning & Manipulation

The robots run on AnywareOS, our industrial physical intelligence system.

Its architecture is modular and designed to generalize across applications without reprogramming.

We are looking for a Robot Learning Engineer to advance the planning and manipulation capabilities within AnywareOS.

• Improve learned policies for robot manipulation that augment and extend our production system's performance envelope
• Contribute to our manipulation capabilities : data collection from deployed robots, physics simulation environments, policy training, and deployment validation
• Use production multimodal data — rich sensor streams from real industrial operations across logistics and manufacturing
• Collaborate with perception and controls engineers to close the loop between scene understanding, navigation, and other capabilities inside AnywareOS
• Ship models to production — your work will run on robots at customer sites across multiple industries, not just on a sim bench

• MS/PhD in ML, robotics, or related field (or equivalent industry experience shipping learned robotic behaviors)
• Strong applied ML fundamentals : policy learning (imitation learning, RL, or diffusion policies), neural network architecture design, training infrastructure
• Experience with robot learning for manipulation or motion : grasp synthesis, motion generation, trajectory optimization with learned components, or sim-to-real transfer
• Proficiency in Python and ML frameworks ; comfortable with C++ for deployment-critical paths
• Understanding of classical planning (rule-based or optimization-based planning, task-space control)
• Demonstrated ability to go from research idea → working system

Nice to Have

• Prior work on force/compliance control or contact-rich manipulation
• Familiarity with ROS2 and real robot deployment pipelines
• Experience with sim-to-real transfer at scale (domain randomization, system identification)