Senior Robot Learning Engineer
6 days ago
Bristol
This robot learning role is with a seriously exciting scale up. The platform is mature, the data is flowing, and the team is ready to scale its most promising research directions into production-grade manipulation policies. They need someone to lead the development and deployment of large behaviour models, taking diffusion transformers, VLAs, and language-conditioned policies from the literature onto a real bi-manual humanoid. This is not a research-only role. You'll inherit a mature policy training codebase, a VR teleoperation pipeline producing high-frequency multi-modal data, and a Gymnasium environment wrapping a real robot. The work you ship runs on hardware. The Role You will architect, train, and deploy end-to-end large behaviour models for bi-manual and mobile manipulation, and lead the maturing of the early-stage RL pipeline. The key responsibilities • Architect, train, and evaluate end-to-end large behaviour models for bi-manual and mobile manipulation, • Advance diffusion transformer policies, mature VLA integration, and develop language conditioning for true multi-task generalisation, • Apply RL to refine pre-trained policies: RL token fine-tuning, residual RL, off-policy RL with reference-action regularisation, RL-based fine-tuning of diffusion policies, • Build a systematic sim-to-real transfer pipeline, connecting existing simulation infrastructure to training, • Deploy and iterate learned policies on physical robot hardware, • Mentor junior researchers and engineers, and publish at top-tier venues What We're Looking For Essential: • PhD/MSc in ML, Robotics, CS, or related field with 4+ years of equivalent industry research experience, • Demonstrated expertise training and deploying learned manipulation policies on real robots, • Strong background in at least two of: behaviour cloning, diffusion policies, VLA/VLM architectures, RL for manipulation, • PyTorch and large-scale (multi-GPU, distributed) training, • Track record of publications at top-tier venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR), or equivalent demonstrated research impact through deployed systems, patents, or significant open-source contributions, • Strong Python; production-quality research code with proper testing, type hints, and documentation Useful: • Hands-on experience with humanoid or bi-manual manipulation platforms, • Diffusion transformer, ACT, or VLA architectures specifically, • Pre-trained vision/language models for robot control (CLIP, DINOv2, PaliGemma), • MuJoCo, Isaac Sim, or ManiSkill for sim-to-real policy training, • RL fine-tuning of pre-trained policies (residual RL, DPPO, or similar), • 3D perception for policy conditioning (point clouds, keypoints, NeRFs) Key contribution areas Policy Architecture & Training • End-to-end large behaviour models for bi-manual and mobile manipulation, • Scale and evolve diffusion transformer policies, VLA integration, and language conditioning, • Extend the imitation learning pipeline to leverage growing teleoperation datasets, • Apply RL to push beyond what imitation alone can reach, • Target sub-millimetre precision and contact-rich manipulation Generalisation & Scaling • Develop policies that generalise across tasks, object categories, and environments, • Move from single-task to multi-task and task-conditioned architectures, • Design hierarchical behaviour systems for long-horizon manipulation, • Investigate data-efficient learning: few-shot adaptation, transfer learning, multi-dataset training, • Drive systematic ablations across architectures Sim-to-Real & Deployment • Build the sim-to-real transfer pipeline: domain randomisation, rendering augmentation, sim-to-real benchmarking, • Deploy and iterate learned policies on physical robot hardware, • Extend the Gymnasium environment wrapper and integrate with the robot's control stack, • Leverage perception team outputs (keypoints, learned features, 3D point clouds) for policy conditioning Research Leadership • Track the literature and bring relevant advances back to the team, • Identify and propose new research directions aligned with the manipulation roadmap, • Mentor junior researchers and engineers, • Publish at top-tier venues — conference attendance and open-source contributions are actively supported What's On Offer • Join a team with world class applied research scientists, ML engineers, and robotics software engineers, • A mature platform that ships to physical hardware, not slides, • Active support for conference attendance and open-source contributions, • Competitive compensation Apply or send your CV to —
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