{"id":624385,"url":"https://alion.io/job/fieldai-robotics-autonomy-engineer-locomotion","title":"Robotics Autonomy Engineer, Locomotion","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"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Gym","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":"PyBullet","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":"Isaac Sim","optional":true}],"status":"live","first_seen_at":"2025-07-02T18:46:25Z","employer_posted_date":"2025-07-02","last_verified_at":"2026-11-01T19:04:42Z","board_verified":true,"closed_at":null,"days_open":487,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":486},"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 - Locomotion, you’ll lead the development and deployment of state-of-the-art controllers for legged and humanoid robots. You’ll be part of a deeply technical team advancing real-world robotic capabilities through cutting-edge research, simulation tools, and field validation. If designing locomotion systems that can navigate complex, dynamic environments 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\nArchitect and implement scalable reinforcement learning (RL) pipelines for locomotion and whole-body control on legged and humanoid robots\nDesign policy architectures, training curricula, and domain randomization strategies that close the sim-to-real gap\nIntegrate GPU-accelerated, physics-based simulation environments with custom, distributed training workflows\nTrain locomotion policies from human motion data using imitation learning and motion retargeting, and distill them into compact policies that run on the robot\nCreate agile, robust, and terrain-aware (perceptive) locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware\nSolve real-world challenges in balance and push recovery, contact-rich dynamics, high degree of freedom (DOF) whole-body coordination, and terrain variability\nAutomate evaluation across domain-randomized scenarios and maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility\nWork closely with systems engineers, perception experts, and embedded teams, incorporating real-world telemetry and field data to continuously improve generalization\nLead deployment workflows from experiment through lab testing to field robot validation\nWhat You Have\nMaster’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred)\nDeep expertise in reinforcement learning for continuous control\n2+ years of experience developing and deploying locomotion policies on real robotic systems (preferred)\nHands-on experience with legged robot platforms (quadrupeds, bipedal/humanoid systems, or exoskeletons)\nProficiency with simulation tools such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet\nStrong command of sim-to-real transfer and a track record of bridging the gap successfully\nSolid understanding of contact dynamics, control theory, and kinematics\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 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 locomotion, reinforcement learning, or control (e.g., CoRL, RSS, ICRA, IROS)\nFamiliarity with ROS/ROS2 or custom middleware for real-time control\nBackground in manipulation, loco-manipulation, or whole-body coordination\nExperience combining learned policies with model predictive control (MPC) or whole-body controllers\nExperience with motion imitation pipelines from motion capture or teleoperation data\nExperience debugging sim-to-real issues at scale\nContributions to reinforcement learning libraries or 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