Senior Robotics control engineer

Overview Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware with a universal Physical AI OS, making robots as easy to build and deploy as software. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug-and-play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI. If you want to help shape the next layer of human capability — and believe the future of robotics should be built together, not in isolation — we’d love to build it with you. 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 #J-18808-Ljbffr dexmate