Role: VLA Pre-training Engineer / Deep Learning Engineer – World Models (2 openings)
Company: Humanoid
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.