Humanoid

We believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND-01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.

About the Role

We're hiring a VLA Pre-training Engineer (Deep Learning) to join our Autonomy team based in London. In this role you will work on all aspects of training capable policies, be it pre-training of a base model on a diverse multi-embodiment corpus of trajectories, fine-tuning a policy to perform a specific task well, curating data collection processes or exploring productive ways to generate and use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, while experience in robotics isn’t strictly required. However, if you don’t have such experience, be prepared that you’d need to familiarize yourself with a new domain quickly.

What You'll Do

• Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.
• Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.
• Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.
• Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.
• Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak-supervision labelling, curate balanced datasets, and auto-surface fresh failure cases into retraining.
• Work with MLOps & Data Platform teams to scale distributed training and optimize models for real-time edge inference.