Senior Robotics Controls Engineer

The Role

We are looking for a Senior Robotics Controls Engineer to own state estimation and whole-body control for our robots. You will design, implement, and ship real-time control and estimation algorithms in production C++, and iterate rapidly between simulation and hardware to make our robots move with precision and stability.

Responsibilities

• Design, implement, and deploy state estimation and sensor fusion algorithms for real-time general-purpose robot control — EKFs, UKFs, particle filters, factor graphs — fusing IMUs, encoders, force/torque sensors, and proprioceptive signals
• Develop and tune advanced control algorithms for dynamic robot motion: nonlinear control, model predictive control (MPC), optimal control, and whole-body control for legged and manipulating systems
• Architect and ship production-grade C++ control code running in real-time embedded environments; hold your implementations to the same quality bar as deployed software
• Iterate rapidly between simulation and hardware — design experiments, collect data, debug failure modes, and drive measurable performance improvements on physical robots
• Develop trajectory optimization and motion planning algorithms that respect actuator limits, contact constraints, and stability margins
• Define and maintain performance metrics and evaluation frameworks for control and estimation subsystems; own the failure analysis loop
• Work directly with embedded, mechanical, and AI teams to integrate control algorithms across the full robot stack

Minimum Qualifications

• 5+ years of professional experience developing control systems for dynamic robots, deployed on real hardware
• Master's or PhD in Robotics, Controls, Mechanical Engineering, or related field
• Deep expertise in control theory: nonlinear control, MPC, LQR, optimal control, and whole-body control
• Strong state estimation background: Kalman filters (EKF/UKF), particle filters, factor graphs, and Bayesian estimation
• Production-quality C++ for real-time control; Python for analysis, simulation, and tooling
• Solid command of robot kinematics, rigid-body dynamics, and spatial mathematics
• Hands-on experience with sensor integration and characterization: IMUs, encoders, force/torque sensors
• Proven track record implementing and validating control algorithms on physical robotic systems — not just simulation

Preferred Qualifications

• Experience with bipedal, quadruped, or humanoid robots — highly dynamic, underactuated, contact-rich systems
• Background in reinforcement learning or learning-augmented control for legged locomotion or manipulation
• Experience with whole-body control and contact dynamics: contact estimation, impact modeling, friction-cone constraints
• Familiarity with trajectory optimization frameworks and solvers: OSQP, IPOPT, Crocoddyl, or custom implementations
• Proficiency with simulation environments: MuJoCo, Drake, Isaac Sim, or equivalent
• Experience with real-time computing constraints: deterministic execution, latency budgets, and embedded deployment
• Track record of publications at top-tier venues (ICRA, IROS, CoRL, RSS, IJRR) is a strong plus