Deep Learning Engineer - World Models

About the Role

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.

What We're Looking For

• 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.

Nice to have

• 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.

What We Offer

• 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.