Robotics Controls Engineer

If you've spent time tuning control on industrial arms, cobots, or manipulators (UR, Franka, KUKA iiwa, AgileX, OpenArm, or your own custom platform) and you can talk fluently about PID, LQR, impedance control, and trajectory optimization, this is for you.

What You'll Do

• Design, implement, and tune low-level controllers for multi-DOF robotic arms (position, velocity, torque, and impedance/admittance control).
• Build and maintain real-time control loops and interface with motor drivers / actuators (EtherCAT, CAN, current/torque control).
• Develop and integrate motion planning for manipulation, collision-free planning, trajectory optimization, and retiming.
• Work on kinematics and dynamics: forward/inverse kinematics, Jacobians, dynamics models, system identification, and calibration.
• Get controllers and planners working reliably on hardware, not just in sim, debug the messy gap between the two.
• Collaborate with perception, learning, and mechanical teams to turn a desired behavior into smooth, safe, repeatable arm motion.

Required Qualifications

• BS, MS, or PhD in Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, Aerospace Engineering, or a related field.
• 4 years of hands-on experience developing and shipping robotics controls/motion on real hardware (an MS/PhD with equivalent research and project experience: roughly BS 4 years, MS 2 years, or a PhD in a relevant area).
• Hands-on experience developing controls and/or motion for robotic arms, industrial arms, cobots, or manipulators (e.g., UR, Franka Emika, KUKA, AgileX, OpenArm, or custom hardware).
• Strong grasp of classical control: PID, LQR, state-space methods, and ideally impedance/admittance or whole-body control. MPC is a plus.
• Experience with motion planning frameworks, MoveIt, cuRobo, OMPL, TrajOpt, or similar, and an understanding of what's happening under the hood.
• Solid robotics fundamentals: rigid-body kinematics and dynamics, control theory, and real-time systems.
• Proficient in C and Python; comfortable in ROS / ROS 2.
• Experience taking control/planning code from simulation onto physical hardware and making it robust.

Nice to Have (Strong Pluses)

• Perception experience, traditional CV, depth/point clouds, pose estimation, SLAM, or calibration for manipulation.
• Experience with learning-based approaches: imitation learning, diffusion policies, reinforcement learning.
• Familiarity with AI world models and Vision-Language-Action (VLA) models (e.g., OpenVLA, π0, RT-style policies).
• Simulation experience (Isaac Sim/Lab, MuJoCo, Gazebo, Drake).
• Background in real-time / embedded control and actuator design.