The posting
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.
