{"id":127980,"url":"https://alion.io/job/path-robotics-machine-learning-engineer-robot-learning-loco-manipulation","title":"Machine Learning Engineer, Robot Learning, Loco-Manipulation","company":{"id":48166,"name":"Path Robotics","domain":"path-robotics.com","url":"https://alion.io/company/path-robotics","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":83,"open_postings":20,"ghost_share":0,"stale_share":0.7,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Robotics","role_family":"Robotics","seniority":"junior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Columbus, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":80000,"max_usd":175000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":114},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Edge AI","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"Machine Learning","optional":false},{"name":"MuJoCo","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"TensorRT","optional":false}],"status":"live","first_seen_at":"2026-04-28T19:51:45Z","employer_posted_date":"2026-07-13","last_verified_at":"2026-10-20T08:29:06Z","board_verified":true,"closed_at":null,"days_open":174,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":174},"description":"Build the Path Forward\nAt Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.\nBig, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.\nWe are standing up a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing. \nWe are seeking a Machine Learning Engineer to join us as a founding member. You will be among the first ML engineers on a research stack that does not exist anywhere else in the field built around visual reasoning, learned action policies, and reinforcement-learning fine-tuning from real customer data.\nWhat You’ll Do\nBuild the team's robot-learning stack from the ground up. This is a founding role; you are designing the training infrastructure, data pipelines, simulation environments, model architectures, and deployment workflows - not inheriting them. Multi-modal perception, scene understanding, and learned action generation work in tight coordination on the stack you help create.\nStand up ML infrastructure - training pipelines, experiment tracking, data versioning, reproducible sim-to-real workflows.\nTrain policies across manipulation, locomotion, and the whole-body control coupling between them. On legged platforms performing precision tasks, manipulation and locomotion are not separable - every arm motion shifts the centre of mass; the whole-body controller compensates in real time to maintain accuracy at the tool. Behavioural cloning, diffusion- and flow-matching action generation, reinforcement-learning fine-tuning. Cobots, industrial arms, and mobile platforms.\nDeploy in stages - through a phased rollout strategy that builds production trust over time. Every real-world execution accumulates training data for continuous improvement.\nCollaborate daily with mechanical engineers, perception engineers, robotics engineers, and manufacturing domain experts. Within-department rotation across home teams is expected.\nWho You Are\nPh.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field - or equivalent experience.\n2+ years of hands-on robot learning experience. You have trained policies and deployed them on real robot hardware - not just in simulation.\nSim-to-real transfer experience - built simulation environments, implemented domain randomisation, transferred policies to physical robots, debugged where it broke.\nImplementation experience with diffusion-based or flow-matching action policies for robots, and with action chunking.\nReinforcement learning for robotics applied on real hardware - sample-efficient on-robot methods, residual RL on top of pretrained policies, on-policy fine-tuning of foundation policies.\nStrong programming skills in Python; PyTorch and ML training infrastructure at production level.\nPractical experience with NVIDIA Isaac Sim / Isaac Lab, MuJoCo, or equivalent.\nComfort with physical robots - debugging, iterating, deploying.\nStrong communication skills, able to convey complex technical concepts to a diverse audience.\nStrongly Preferred:\nEdge inference on edge-class hardware (TensorRT, ONNX, FP16 / INT8 quantisation). Real-time on-robot deployment is a core requirement.\nVisual self-supervised representation learning experience on robot or 3D-vision tasks.\nLegged-robot or whole-body control experience - locomotion, manipulation on a floating base, or the integration between them on quadrupeds or humanoids.\nPhysics-informed ML - hybrid models where learned components are constrained by known physics.\nExperience building ML pipelines or infrastructure in a team setting.\nWhy You’ll Love Working Here\nDaily free lunch to keep you fueled and connected with the team\nFlexible PTO so you can take the time you need, when you need it\nComprehensive medical, dental, and vision coverage\n6 weeks fully paid parental leave, plus an additional 6-8 weeks for birthing parents (12-14 weeks total)\n401(k) retirement plan through Empower\nGenerous employee referral bonuses-help us grow our team!\nWho We Are\nAt Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.\nIf you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact . We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. 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