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
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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
About Flexiv
Robotics300 employeesFounded 2016
Develops adaptive, AI-powered robots for complex industrial automation.