Robotics Algorithm Engineer-motion planning

RESPONSIBILITIES： Design, implement, test, and deploy motion planning algorithms for high-DOF manipulators, with emphasis on contact-rich and compliant manipulation tasks such as assembly and surface treatment Co-own the software interface between the motion planning stack and the whole body control module; define and maintain shared state representations, constraints, and control handoff protocols Develop planners that reason over contact modes, contact sequencing, and force/torque constraints — not just collision-free path finding Benchmark and evaluate planners in simulation and on real hardware across contact-rich scenarios; own reliability and task-success metrics Collaborate across perception, control, and hardware teams to translate physical task requirements into well-defined planning problems Drive software quality through code review, testing standards, and continuous improvement of engineering best practices Manage and communicate development schedules and milestones REQUIREMENTS： PhD or MS in Robotics, Mechanical Engineering, Computer Science, or a related field — or equivalent industry experience 3+ years of hands-on experience with robotic systems software engineering Proficiency in C++ and/or Python; demonstrated experience deploying motion planning software on real robots and simulators Strong theoretical and practical understanding of motion planning for high-DOF manipulators, including planners that operate under contact and force constraints Solid grounding in robot kinematics and dynamics forward/inverse kinematics, Jacobian methods, rigid body dynamics, and force/torque reasoning Ability to work independently, take ownership, and continuously raise the bar on engineering standards PREFERRED SKILLS： Strong candidates will have experience in one or more of the following areas: Experience with whole body control, impedance control, or admittance control for compliant manipulation Familiarity with contact mechanics and hybrid force-motion control e.g. force-controlled insertion, peg-in-hole, surface following, deburring, or polishing tasks Experience with trajectory generation for robot manipulators, including time-optimal parameterization and smooth Cartesian trajectory design e.g. time-optimal path parameterization (TOPP), C² continuous Cartesian trajectories, jerk-limited motion profiles, spline-based or Bézier representations; awareness of how trajectory smoothness affects contact stability and surface quality Background in numerical optimization and optimal control e.g. trajectory optimization, MPC, QP solvers — especially with contact constraints Experience applying reinforcement learning to contact-rich or high-DOF manipulation e.g. model-free / model-based RL, sim-to-real transfer, policy learning for dexterous tasks Experience with ROS / ROS2 in multi-process, real-time robotic systems Familiarity with PyTorch / CUDA for scientific computing or learning-based planners Proven ability to pick up a new knowledge domain and deliver production-quality code Job Type: Full-time Pay: $160,000.00 - $190,000.00 per year Work Location: In person