humanoid - Deep Learning Engineer, World Models
Requirements
• 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.
Responsibilities
• 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.
Benefits
• 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.
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