VLA Pre-training & World Models | Humanoid

<p style="min-height:1.5em"><strong>Role:</strong> VLA Pre-training Engineer / Deep Learning Engineer – World Models (2 openings)<br /><strong>Company:</strong> <a target="_blank" rel="noopener noreferrer nofollow" href="https://thehumanoid.ai/">Humanoid</a><br /><strong>Location:</strong> London, UK (King’s Cross) – on-site, five days a week<br /><strong>Job Type:</strong> Full-time</p><p style="min-height:1.5em"><strong>About Humanoid</strong></p><p style="min-height:1.5em">Humanoid is building the world’s most capable, commercially scalable and safe humanoid robots. Its platform, HMND-01, is already deployed in real industrial environments, which makes Humanoid the most serious humanoid robotics company in Europe.</p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots.</p></li><li><p style="min-height:1.5em">Offices in London (HQ, King’s Cross), Boston, Vancouver and San Diego.</p></li><li><p style="min-height:1.5em">Values: Speed, Ambition, Passion, Trust.</p></li></ul><p style="min-height:1.5em"><strong>About the role</strong></p><p style="min-height:1.5em">The VLA Core team builds the base vision-language-action model that the entire manipulation pipeline depends on. This is deep learning at scale: training models from scratch on multi-embodiment trajectory corpora and internet-scale video, with real deployment constraints. The model has to fit and run on a real robot.</p><p style="min-height:1.5em">Robotics experience is not required. If you come from LLMs, VLMs or video generation, be ready to get up to speed on a new domain quickly.</p><p style="min-height:1.5em">This is a senior individual contributor role with no management track. The team is research-adjacent, but the output is models that run on robots. Results are shared as blog posts, not papers.</p><p style="min-height:1.5em">There are two tracks in one team. They are the same role with a different focus, so you can lean into either.</p><p style="min-height:1.5em"><strong>Track 1: VLA Pre-training</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Pre-train base models on a diverse multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks and explore synthetic data.</p></li><li><p style="min-height:1.5em">Post-train policies with behaviour cloning and RL, and own the full loop from data to deployment.</p></li><li><p style="min-height:1.5em">Partner with the Data Collection team: specify what good data looks like, identify failure modes, ensure diversity and coverage.</p></li><li><p style="min-height:1.5em">Run pre-, mid- and post-training on the VLA stack; explore new modalities and architecture changes.</p></li><li><p style="min-height:1.5em">Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak-supervision labelling, curate balanced datasets and automatically surface fresh failure cases into retraining.</p></li><li><p style="min-height:1.5em">Work with the MLOps and Data Platform teams to scale distributed training and optimize models for real-time edge inference on the robot.</p></li></ul><p style="min-height:1.5em"><strong>Track 2: World Models</strong&