Senior Robot Learning Engineer

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, 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

• 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)

• 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