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

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