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

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 Information

Who this role is for

PhD or MS

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