What You'll Do- Design, implement, and evaluate humanoid manipulation and loco-manipulation behaviors on real hardware
- Integrate perception, planning, control, grasping, whole-body coordination, and task execution into deployable robot workflows
- Run hardware experiments, analyze failures, and improve manipulation reliability across diverse objects, environments, and tasks
- Partner with system integration, hardware, field application, and testing teams to move capabilities from prototype to deployment
- Support teleoperation, data collection, and human-in-the-loop workflows for improving manipulation performance
- Build tools, metrics, and evaluation protocols for manipulation success, repeatability, failure recovery, and operator usability
- Debug cross-domain issues spanning software, sensors, actuators, end-effectors, calibration, timing, and field conditions
What You Bring- MS or PhD in Robotics, Mechanical Engineering, Computer Science, or a related field preferred; BS considered with a demonstrated track record of hands-on robotics work across multiple physical systems - research projects, competition robotics, or internships with daily hardware exposure
- Hands-on experience with robotic manipulation, humanoids, mobile manipulation, dexterous hands, or contact-rich robotics - must include physical hardware; simulation-only backgrounds will not be considered
- Strong foundation in kinematics, dynamics, motion planning, control, and real robot experimentation
- Experience with C++, Python, ROS/ROS2, and Linux in a real robotics codebase
- Demonstrated ability to iterate quickly from experiment to working behavior on physical hardware; comfortable running daily hardware experiments, analyzing failures, and adapting approach in real-time
- Background appropriate for a junior-to-mid engineer; fresh MS and PhD graduates welcome
What Sets You Apart- Experience with humanoid platforms or contact-rich, dexterous manipulation systems - you've worked with robots that have hands, not just grippers
- Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation
- You've taken a manipulation capability from prototype to reliable, repeatable field behavior - you know what it takes to close that gap and you've done it
- Track record of building evaluation frameworks for manipulation: test suites, metrics for success and failure, and the discipline to document and learn from what breaks
Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
