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# Deep Learning Engineer - World Models

- **Company:** Humanoid
- **Location:** London, United Kingdom
- **Salary:** £64,948
- **Contract:** Permanent contract
- **Skills:** Software Debugging, Python (Programming Language), Pytorch, Transfer Learning, Deep Learning, Isaac Sim, Transformer Architectures, Free and Open-Source Software, Data Pipelines, Data Generation
- **Published:** November 11, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5961468104

## About the Role

* 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.

## Description

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., 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.