Robotics control engineer

Dexmate is at the forefront of developing advanced robotic systems that solve real-world challenges. We're building next-generation robots designed to work alongside humans, operate in human environments, and help address growing labor shortages.Position OverviewWe are seeking talented Control Engineers to join our dynamic team and lead the development of state-of-the-art control and state estimation algorithms for our robot platform.Key ResponsibilitiesDevelop and implement state estimation, sensor fusion, planning, control algorithms that enable fast, dynamic and safe robot motionCollaborate with cross-functional teams including embedded system, perception, hardware, AIOptimize control performance across multiple domains including stability, safety, precision, and energy efficiencyDesign and conduct experiments to validate control algorithms both in simulation and on hardwareAnalyze system performance data to identify failure modes and improvement opportunitiesDocument technical approaches, implementation details, and experimental resultsRequirementsMaster's or PhD in Robotics, Controls, Mechanical Engineering, or related technical field4+ years of professional experience developing control systems for dynamic robotsStrong expertise in control theory including nonlinear control, model predictive control, and optimal controlExperience with state estimation techniques such as Kalman filters, particle filters, and factor graphsProficiency in C++, Python, Rust for real-time robotics applicationsStrong understanding of robot kinematics, dynamics, and mathematical modelingExperience working with sensor integration including IMUs, encoders, force/torque sensorsProven track record of implementing and testing control algorithms on physical robotic systemsExcellent problem-solving skills and ability to debug complex system interactionsPreferred QualificationsExperience with highly dynamic control systems such as bipedal, quadruped, or humanoid robotsKnowledge of reinforcement learning or other machine learning approaches for controlExperience with whole-body control and contact dynamics for legged systemsExperience with real-time computing and optimizationBackground in trajectory optimization and motion planningFamiliarity with ROS, simulation environments (e.g., Drake, Isaac Sim, SAPIEN, MuJoCo, PyBullet)Track record of publications in top-tier robotics conferences/journals