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Machine Learning Engineer, Robot Learning, Loco-Manipulation

Path Robotics· Engineering
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Columbus, OHfull-timePosted 2mo ago

About the role

Founding member of a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing. Building the robot-learning stack from scratch, training policies that integrate manipulation, locomotion, and whole-body control, and deploying in phased production with continuous improvement from real customer data.

Responsibilities

  • Build 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.
  • Stand up ML infrastructure — training pipelines, experiment tracking, data versioning, reproducible sim-to-real workflows.
  • Train 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.
  • Deploy in stages — through a phased rollout strategy that builds production trust over time. Every real-world execution accumulates training data for continuous improvement.
  • Collaborate daily with mechanical engineers, perception engineers, robotics engineers, and manufacturing domain experts. Within-department rotation across home teams is expected.

Qualifications

  • Ph.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field — or equivalent experience.
  • 2+ years of hands-on robot learning experience. You have trained policies and deployed them on real robot hardware — not just in simulation.
  • Sim-to-real transfer experience — built simulation environments, implemented domain randomisation, transferred policies to physical robots, debugged where it broke.
  • Implementation experience with diffusion-based or flow-matching action policies for robots, and with action chunking.
  • Reinforcement 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.
  • Strong programming skills in Python; PyTorch and ML training infrastructure at production level.
  • Practical experience with NVIDIA Isaac Sim / Isaac Lab, MuJoCo, or equivalent.
  • Comfort with physical robots — debugging, iterating, deploying.
  • Strong communication skills, able to convey complex technical concepts to a diverse audience.

Skills

Required:Python programmingPyTorchSimulation environments

Benefits

  • Daily free lunch
  • Flexible PTO
  • Comprehensive medical, dental, and vision coverage
  • 6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
  • 401(k) retirement plan through Empower
  • Generous employee referral bonuses—help us grow our team!
Last updated Aug 7, 2026. Interested? Apply on the company site.

About Path Robotics

Path Robotics | Physical AI for Manufacturing

Path Robotics – Physical AI for manufacturing. Obsidian, our foundational AI, powers Path's Intelligent Welding Cells and Rove. 4x productivity, 30%+ lower cost, $0 capex.

Welding CellsRoveObsidianPhysical AIManufacturingAutomation
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