Machine Learning Engineer

Job Description

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, 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.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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