{"schemaVersion":"jobsearcher.job.v1","id":"2a4510a8408e1d5364a620e4","url":"https://jobsearcher.com/jobs/2a4510a8408e1d5364a620e4","canonicalUrl":"https://jobsearcher.com/jobs/2a4510a8408e1d5364a620e4","title":"Robotics Algorithm Engineer-motion planning","description":"RESPONSIBILITIES：\nDesign, 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\nCo-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\nDevelop planners that reason over contact modes, contact sequencing, and force/torque constraints — not just collision-free path finding\nBenchmark and evaluate planners in simulation and on real hardware across contact-rich scenarios; own reliability and task-success metrics\nCollaborate across perception, control, and hardware teams to translate physical task requirements into well-defined planning problems\nDrive software quality through code review, testing standards, and continuous improvement of engineering best practices\nManage and communicate development schedules and milestones\nREQUIREMENTS：\nPhD or MS in Robotics, Mechanical Engineering, Computer Science, or a related field — or equivalent industry experience\n3+ years of hands-on experience with robotic systems software engineering\nProficiency in C++ and/or Python; demonstrated experience deploying motion planning software on real robots and simulators\nStrong theoretical and practical understanding of motion planning for high-DOF manipulators, including planners that operate under contact and force constraints\nSolid grounding in robot kinematics and dynamics forward/inverse kinematics, Jacobian methods, rigid body dynamics, and force/torque reasoning\nAbility to work independently, take ownership, and continuously raise the bar on engineering standards\nPREFERRED SKILLS：\nStrong candidates will have experience in one or more of the following areas:\nExperience with whole body control, impedance control, or admittance control for compliant manipulation\nFamiliarity with contact mechanics and hybrid force-motion control e.g. force-controlled insertion, peg-in-hole, surface following, deburring, or polishing tasks\nExperience 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\nBackground in numerical optimization and optimal control e.g. trajectory optimization, MPC, QP solvers — especially with contact constraints\nExperience 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\nExperience with ROS / ROS2 in multi-process, real-time robotic systems\nFamiliarity with PyTorch / CUDA for scientific computing or learning-based planners\nProven ability to pick up a new knowledge domain and deliver production-quality code\nJob Type: Full-time\nPay: $160,000.00 - $190,000.00 per year\nWork Location: In person","company":"Flexiv Robotics","rawCompany":"flexiv robotics","city":"San Jose","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-03T15:55:55.526Z","occupations":[{"code":"17-2199.08","title":"Robotics Engineers","slug":"robotics-engineers"},{"code":"17-3024.01","title":"Robotics Technicians","slug":"robotics-technicians"},{"code":"17-2199.05","title":"Mechatronics Engineers","slug":"mechatronics-engineers"}],"industries":[{"code":"333248","title":"All Other Industrial Machinery Manufacturing","slug":"all-other-industrial-machinery-manufacturing"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Robotics Algorithm Engineer-motion planning","description":"RESPONSIBILITIES：\nDesign, 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\nCo-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\nDevelop planners that reason over contact modes, contact sequencing, and force/torque constraints — not just collision-free path finding\nBenchmark and evaluate planners in simulation and on real hardware across contact-rich scenarios; own reliability and task-success metrics\nCollaborate across perception, control, and hardware teams to translate physical task requirements into well-defined planning problems\nDrive software quality through code review, testing standards, and continuous improvement of engineering best practices\nManage and communicate development schedules and milestones\nREQUIREMENTS：\nPhD or MS in Robotics, Mechanical Engineering, Computer Science, or a related field — or equivalent industry experience\n3+ years of hands-on experience with robotic systems software engineering\nProficiency in C++ and/or Python; demonstrated experience deploying motion planning software on real robots and simulators\nStrong theoretical and practical understanding of motion planning for high-DOF manipulators, including planners that operate under contact and force constraints\nSolid grounding in robot kinematics and dynamics forward/inverse kinematics, Jacobian methods, rigid body dynamics, and force/torque reasoning\nAbility to work independently, take ownership, and continuously raise the bar on engineering standards\nPREFERRED SKILLS：\nStrong candidates will have experience in one or more of the following areas:\nExperience with whole body control, impedance control, or admittance control for compliant manipulation\nFamiliarity with contact mechanics and hybrid force-motion control e.g. force-controlled insertion, peg-in-hole, surface following, deburring, or polishing tasks\nExperience 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\nBackground in numerical optimization and optimal control e.g. trajectory optimization, MPC, QP solvers — especially with contact constraints\nExperience 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\nExperience with ROS / ROS2 in multi-process, real-time robotic systems\nFamiliarity with PyTorch / CUDA for scientific computing or learning-based planners\nProven ability to pick up a new knowledge domain and deliver production-quality code\nJob Type: Full-time\nPay: $160,000.00 - $190,000.00 per year\nWork Location: In person","datePosted":"2026-08-03T15:55:55.526Z","dateModified":"2026-08-03T15:55:55.526Z","hiringOrganization":{"@type":"Organization","name":"Flexiv Robotics","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Jose","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2a4510a8408e1d5364a620e4"},"url":"https://jobsearcher.com/jobs/2a4510a8408e1d5364a620e4"}}