Senior Robotics Controls Engineer — pHRI
About UsSymbiokinetics 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.About the RoleYou 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.What You'll DoDevelop and maintain real-time controllers for contact-rich manipulation and shared autonomy tasksDesign and run system identification proceduresImplement and tune friction compensation, gravity compensation, and inertia shaping algorithmsInstrument, run, and analyze hardware experiments to validate controller performance against quantitative criteriaRead and evaluate robotics research literature to identify methods worth implementingPort and adapt controllers across robot platforms and software stacksSupport design team in developing new robot and end-effector designs for co-manipulationRequiredMS or PhD in Robotics, Mechanical Engineering, Controls, or related field5+ 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 frameworksExperience with online parameter estimation or adaptive control on physical hardware (payload identification, recursive inertia/friction updates, or equivalent)Strong C/C++ for real-time systemsExperience with ROS2 as a communication and tooling layerTrack record of closing the gap between a method in a paper and a controller running on hardwareNice to HavePassivity-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 onlineExperience designing actuated mechanical systemsVisuotactile, tactile array, or F/T sensor integration, calibration, and compensationBayesian optimization or other black-box methods for controller parameter tuningExperience with the Franka FR3 or similar platformFamiliarity with medical or rehabilitation robotics and relevant safety standardsBenefitsCompetitive salary and equityHealth, dental, and vision insurance, 401(k)Work directly with hardware on problems that matter