Senior Robotics control engineer

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

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

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