Build the Path ForwardWe 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.
Why You'll Love Working Here- Daily free lunch to keep you fueled and connected with the team
- Flexible PTO so you can take the time you need, when you need it
- 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!
