Deep Learning Engineer - World Models

Here at 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

• our rapidly developed humanoid platform being deployed in real industrial environments
• and we’re growing the team to take it even further.

At Humanoid we strive to create the world's leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

As a Research Engineer on the World Models team, you will build action-conditioned generative models that predict how the world evolves around our robots — future video, proprioception, contacts, and outcomes — from past observations and actions. World models serve four purposes in our stack: a pretrained, physics-aware prior for our VLA policies; an engine for rare data collection, cross-platform transfer, and sim-to-real transfer; a testbed for policy evaluation and testing before hardware; and a future-prediction rollout engine that surfaces what our policies intend to do, for safety and planning.

This is a hands-on individual contributor role: you will design architectures, run large training jobs, and validate your models against real fleet data from industrial deployments.

• Design and train multimodal world models
• video, state, action, and language
• using diffusion-based and transformer architectures.
• Build action-conditioned 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, and roll out policy futures to expose intended behaviour for safety review and planning.
• Generate synthetic rollouts and counterfactual experience
• including rare events, cross-platform transfer, and sim-to-real transfer
• to augment policy training, and measure their effect on downstream task performance.
• Establish fidelity metrics and calibration protocols that quantify where the world model can be trusted and where it diverges from reality.
• Build data pipelines that turn fleet telemetry, teleoperation logs, and internet-scale video into training corpora for world models.
• Run scaling and ablation studies on architecture, data mixture, and context length; communicate findings crisply.
• Collaborate with pretraining, RL, and manipulation teams to integrate world models into policy training and evaluation loops.

A track record of training large generative models

• video, world, or multimodal
• with shipped models or published artifacts to show for it.
• Deep hands-on experience with modern generative architectures: diffusion models, autoregressive transformers, latent-variable models, or video prediction.
• Experience with large-scale distributed training: streaming datasets, checkpointing and state management, debugging numerics and training instabilities.
• Strong Python + PyTorch/JAX; you can profile kernels, optimize data loaders, and write maintainable research code.
• Empirical rigor: you design careful evaluations, run honest baselines, and document experiments clearly.
• Excitement about grounding generative models in physical reality rather than pixels alone.

Experience with world models for robotics or autonomous driving (e.g., action-conditioned video models, learned simulators, model-based RL).

• Familiarity with robotics simulators (Isaac Sim, MuJoCo) and sim-to-real considerations.
• Experience using world models for policy evaluation or synthetic data generation at scale.
• Publications at top-tier deep learning conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions.
• Experience optimizing generative models for fast inference.

Competitive equity: stock options with meaningful upside as we scale.

• 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).
• Private healthcare, including virtual and in-person care.
• Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.
• Free daily breakfast, catered lunch, and snacks in-office.
• Work at the frontier
• collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.
• Real ownership
• direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

The job is published on Humanoid’s website. Open the original posting ↗ to submit your application. Screeningpass does not fill in a form for you.

Start a contact search from this page. Screeningpass suggests up to three people connected to the role. Choose whom to write to, then Screeningpass looks for their professional email and prepares a message. You review it and send it yourself with Gmail. An email address is not always available, and a reply is never guaranteed.

A search costs 1 credit. Your credit is returned if the search fails. Signing up gives you 3 credits, enough for 3 searches.

Screeningpass brings together internships, apprenticeships and jobs published by companies. For a job, Screeningpass finds someone to contact at the company, looks for their professional email and prepares a message using your CV. See how it works in detail.