Humanoid Engineer, Manipulation

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

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

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