{"id":624142,"url":"https://alion.io/job/fieldai-robotics-autonomy-engineer-manipulation","title":"Robotics Autonomy Engineer, Manipulation","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":103,"ghost_share":0.223,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Robotics","role_family":"Robotics","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United States","Peoria, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Drake","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Lab","optional":false},{"name":"JAX","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Predictive Control","optional":false},{"name":"MuJoCo","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"ROS","optional":false},{"name":"ROS2","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TensorRT","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"Isaac Sim","optional":true}],"status":"live","first_seen_at":"2026-09-05T18:21:38Z","employer_posted_date":"2026-09-05","last_verified_at":"2026-11-01T03:14:05Z","board_verified":true,"closed_at":null,"days_open":56,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":56},"description":"About the Job\nField AI is building the future of autonomy-from rugged terrain to real-world deployment. We’re on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments. As our Robotics Autonomy Engineer - Manipulation, you’ll lead the development and deployment of whole-body control and manipulation capabilities for humanoids and arm-equipped quadrupeds, turning real field scenarios into behaviors our robots perform reliably every day. You’ll be part of a deeply technical team advancing real-world robotic capabilities through cutting-edge research, simulation tools, and field validation. If building robots that can use their hands in the real world excites you, and you want to work where your code hits the ground (literally)-this is your role. This is Field AI.\nWhat You’ll Get To Do\nDevelop whole-body control and manipulation capabilities for humanoids and quadrupeds with arms, coordinating base, torso, and arm motion as a single system\nTake specific field scenarios from concept to repeatable execution on hardware, interacting with the built environment and handling objects and tools\nBuild learning based and model based manipulation policies and integrate them tightly with perception and locomotion\nTrain manipulation policies from demonstration data and adapt vision language action (VLA) foundation models to our robots and tasks, then make them work on hardware\nDesign grasp, contact, and force interaction strategies that hold up across varied objects, surfaces, and environments, with interaction forces kept safe for people and equipment nearby\nHarden behaviors so they succeed across repeated runs, changing lighting, object variation, and real site conditions\nBuild simulation environments and evaluation pipelines for manipulation, and use field data to close the gap between simulation and the real world\nDeploy on physical robots and iterate rapidly through lab testing and field validation\nWork closely with locomotion, perception, and systems engineers to deliver end to end capabilities\nWhat You Have\nMaster’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred)\nDeep expertise in robot manipulation or whole-body control\n2+ years of experience developing and deploying manipulation behaviors on real robotic systems (preferred)\nHands-on experience with robot arms, hands or grippers, and humanoid or mobile manipulation platforms\nSolid understanding of kinematics, dynamics, contact modeling, and force control\nExperience with manipulation planning and control, such as trajectory optimization or model predictive control (MPC)\nExperience with learning based manipulation, such as imitation learning, reinforcement learning (RL), or vision language action models\nProficiency with simulation tools such as Isaac Lab, MuJoCo, or Drake\nExperience integrating perception for manipulation\nStrong Python and/or C++ development skills in Linux-based development environments\nFamiliarity with machine learning frameworks (PyTorch, JAX, TensorFlow)\nA passion for building things that move in the real world\nThe Extras That Set You Apart\n3+ years of experience in an industry or startup robotics setting\nExperience with loco-manipulation on legged platforms or whole-body model predictive control for humanoids\nExperience fine tuning or deploying vision language action models on real robots\nExperience building teleoperation and demonstration data collection pipelines at scale\nExperience with cross embodiment or generalist manipulation policies that transfer across robots and tasks\nExperience with dexterous multi fingered hands, or with tactile and force torque sensing in closed loop manipulation\nExperience optimizing and deploying learning based controllers on resource constrained robotic platforms (ONNX Runtime, NVIDIA TensorRT, real time onboard inference)\nPublications or open-source contributions in manipulation, whole-body control, or robot learning (e.g., CoRL, RSS, ICRA, IROS)\nFamiliarity with ROS/ROS2 or custom middleware for real-time control\nExperience debugging sim-to-real issues at scale","description_format":"text","description_chars":4164,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","Industrial AI"],"lifecycle":[{"event":"open","at":"2026-09-10T18:57:39Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 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