Machine Learning Engineer, Robot Learning, Loco-Manipulation

Build the Path Forward

We are standing up a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing.

We 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.

What You’ll Do

• 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.

Who You Are

• 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.

Strongly Preferred:

• Edge inference on edge-class hardware (TensorRT, ONNX, FP16 / INT8 quantisation). Real-time on-robot deployment is a core requirement.
• Visual self-supervised representation learning experience on robot or 3D-vision tasks.
• Legged-robot or whole-body control experience — locomotion, manipulation on a floating base, or the integration between them on quadrupeds or humanoids.
• Physics-informed ML — hybrid models where learned components are constrained by known physics.
• Experience building ML pipelines or infrastructure in a team setting.