VLA Pre-training & World Models | Humanoid

Role: VLA Pre-training Engineer / Deep Learning Engineer – World Models (2 openings) Company: Humanoid https://thehumanoid.ai/ Location: London, UK (King’s Cross) – on-site, five days a week Job Type: Full-time About Humanoid 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. - 300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots. - Offices in London (HQ, King’s Cross), Boston, Vancouver and San Diego. - Values: Speed, Ambition, Passion, Trust. About the role 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. 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. 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. There are two tracks in one team. They are the same role with a different focus, so you can lean into either. Track 1: VLA Pre-training - Pre-train base models on a diverse multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks and explore synthetic data. - Post-train policies with behaviour cloning and RL, and own the full loop from data to deployment. - Partner with the Data Collection team: specify what good data looks like, identify failure modes, ensure diversity and coverage. - Run pre-, mid- and post-training on the 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 automatically surface fresh failure cases into retraining. - Work with the MLOps and Data Platform teams to scale distributed training and optimize models for real-time edge inference on the robot. Track 2: World Models - Design and train multimodal, action-conditioned world models (video, state, action and language) using diffusion-based and transformer architectures. - Build video prediction and dynamics models that stay physically consistent over long horizons, including contact-rich manipulation, and serve as pretrained priors for VLA policies. - Develop learned-simulator evaluation: score candidate policies offline, predict real-world success rates before deployment, roll out policy futures for safety review and planning. - Generate synthetic rollouts and counterfactual experience (rare events, cross-platform and sim-to-real transfer) and measure their effect on downstream task performance. - Build data pipelines that turn fleet telemetry, teleoperation logs and internet-scale video into training corpora. - Run scaling and ablation studies on architecture, data mixture and context length. What you need to succeed - 3+ years building deep learning systems in industry or research, with shipped models or published work to show for it. - Deep hands-on experience with at least one of: LLMs, VLMs, image or video generative models (architecture, training and inference). For the World Models track: diffusion models, autoregressive transformers, latent-variable models or video prediction. - You have actually trained models yourself: pre-training runs, distributed infrastructure, the works. Connecting APIs, building agents or a quick fine-tune is not enough. - Experience with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training strategies, debugging numerics and training instabilities. - Strong Python and PyTorch or JAX. You can profile, optimize data loaders and write maintainable research code. - Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared. - Empirical rigor: careful evaluations, honest baselines, clear experiment docs, crisp communication of trade-offs. - You are proactive: you can take an open, messy problem and drive it without waiting for a spec. - Working English. Nice to have - Robotics or autonomous driving experience; familiarity with simulators (Isaac Sim, MuJoCo) and sim-to-real. - Experience applying RL to LLMs or robotics. - Experience with VLA models; familiarity with OpenVLA, Physical Intelligence (π) models or similar open-source frameworks. - Experience with world models for robotics or autonomous driving (action-conditioned video models, learned simulators, model-based RL). - Proven productization of deep nets: latency and throughput constraints, on-device optimization, fast inference for generative models. - Publications at top-tier DL conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions. Interview process - Intro call with Igor, hirehire’s AI researcher. - HR screen with Humanoid. - Coding round (LeetCode-style). - Specialization interview (pre-training or world models). - Technical execution interview: how you work, collaborate and drive problems. - Optional chat with a tech leader. What you get - Salary: a wide range, set by the level you show in the interviews. - Equity: stock options with meaningful upside as Humanoid scales. - 30+ paid days off: 23 days annual leave, all UK bank holidays and a company shutdown between Christmas and New Year. - Private healthcare: fully funded, including virtual and in-person care. - Pension: 8% total contribution (5% employee, 3% employer) on full earnings. - Daily food: free breakfast, catered lunch and snacks in the office. - Real ownership: direct access to founding leadership, real input on product direction and room to drive key initiatives from day one. - Visa sponsorship and relocation to London.