{"id":3561999,"url":"https://alion.io/job/relari-robotics-software-engineer","title":"Robotics Software Engineer","company":{"id":183616,"name":"Relari","domain":"relari.ai","url":"https://alion.io/company/relari","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Work at a Startup","truth_index":{"grade":"A","score":100,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-02T06:13:15Z"}},"role":"Robotics","role_family":"Robotics","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":125000,"max":175000,"currency":"USD","period":"year","gross":null,"usd_annual":175000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Machine Learning","optional":false},{"name":"Motion Planning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Teleoperation","optional":false},{"name":"Computer Vision","optional":true},{"name":"Localization","optional":true},{"name":"PyTorch","optional":true},{"name":"ROS","optional":true},{"name":"ROS2","optional":true}],"status":"live","first_seen_at":"2026-10-26T23:12:43Z","employer_posted_date":null,"last_verified_at":"2026-10-26T23:12:43Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"About the role\nYou will build the software systems that enable robots to learn dexterous manipulation skills from human data. You will work with dexterous hands and grippers across control, perception, teleoperation, simulation, and learned policy deployment, turning research ideas into working systems on physical robots. We are looking for robotics software engineers who like working on real hardware and want to apply learning-based approaches to it. You don't need prior experience with robot foundation models, but you should be excited about applying modern machine learning to robotics.\nWhat you’ll do\nDevelop and maintain software for robot arms, dexterous hands, and grippers, including control, sensor integration, and real-time execution.\nBuild teleoperation, motion-retargeting, and data-collection systems that map human hand motion, force, and muscle-activity data onto dexterous hands and grippers with different kinematics.\nBuild perception for our robots and capture setups, including multi-camera calibration and synchronization, hand and object tracking, and 3D state estimation for teleoperation and policy deployment.\nIntegrate cameras, tactile sensors, force/torque sensors, and other sensing modalities into robotic systems.\nDeploy and evaluate learned manipulation policies on physical robots, diagnosing failures across perception, planning, control, and hardware.\nDevelop simulation environments and evaluation tools to support robot learning experiments.\nWork with machine learning and biomechanics researchers on imitation learning, reinforcement learning, and other approaches to dexterous manipulation.\nBuild the robot runtime, data, and evaluation tooling the rest of the team relies on to run experiments quickly.\nWhat we’re looking for\nStrong software engineering skills in Python and/or C++.\nExperience developing software for physical robots, simulation, or closely related systems.\nSolid understanding of robotics fundamentals such as kinematics, dynamics, motion planning, control, or state estimation.\nAbility to independently debug complex systems spanning hardware and software.\nExperience with dexterous hands, contact-rich manipulation, or force and impedance control is a plus.\nExperience with computer vision, 3D perception, camera calibration, or pose estimation is a plus.\nExperience with ROS/ROS2, robot simulators, or robotics development frameworks is a plus.\nExperience with robot learning, imitation learning, reinforcement learning, or PyTorch is a plus.\nWorking at Relari\nRelari is a small research and engineering startup developing new ways for robots to learn dexterous skills from human biomechanics. Our founders have AI research roots at MIT and NVIDIA, along with autonomous-vehicle and robotics deployment experience at Pony ai and Dexterity. We are backed by top investors including Y Combinator, General Catalyst, and Soma Capital. You will work directly with the founders, own problems end to end, and test your ideas on real robotic systems. 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