Senior Robotics Controls Engineer — pHRI

Symbiokinetics builds robotic systems that learn skilled physical interaction from human experts. Our robots operate in direct contact with people in high-stakes environments. They need to feel intuitive, predictable, and be completely safe.

You will design, implement, and validate control algorithms for torque-controlled manipulators used in direct physical contact with people. This is a hands-on role: you will work with real hardware daily, own problems from first-principles analysis through deployment, and be responsible for both the theoretical soundness and the practical performance of what you ship.

• Develop and maintain real-time controllers for contact-rich manipulation and shared autonomy tasks
• Design and run system identification procedures
• Implement and tune friction compensation, gravity compensation, and inertia shaping algorithms
• Instrument, run, and analyze hardware experiments to validate controller performance against quantitative criteria
• Read and evaluate robotics research literature to identify methods worth implementing
• Port and adapt controllers across robot platforms and software stacks
• Support design team in developing new robot and end-effector designs for co-manipulation

• MS or PhD in Robotics, Mechanical Engineering, Controls, or related field
• 5+ years developing and deploying control algorithms on physical torque-controlled robots (not simulation-only)
• Deep understanding of rigid-body dynamics, operational-space control, and impedance/admittance frameworks
• Experience with online parameter estimation or adaptive control on physical hardware (payload identification, recursive inertia/friction updates, or equivalent)
• Strong C/C++ for real-time systems
• Experience with ROS2 as a communication and tooling layer
• Track record of closing the gap between a method in a paper and a controller running on hardware

• Passivity-based control design (energy tanks, port-Hamiltonian methods, Lyapunov stability)
• System identification: excitation trajectory design, offline batch identification with physical-consistency constraints, recursive estimation (RLS, EKF, disturbance observers)
• Adaptive control: variable impedance/admittance laws, iterative learning control, or other schemes that update controller behavior online
• Experience designing actuated mechanical systems
• Visuotactile, tactile array, or F/T sensor integration, calibration, and compensation
• Bayesian optimization or other black-box methods for controller parameter tuning
• Experience with the Franka FR3 or similar platform
• Familiarity with medical or rehabilitation robotics and relevant safety standards

• Competitive salary and equity
• Health, dental, and vision insurance, 401(k)
• Work directly with hardware on problems that matter