About Teaching a robot a new task usually takes hundreds or thousands of demonstrations, plus a round of task-specific training. This team is building the opposite: a foundation model that picks up a new manipulation task from far fewer examples, even when the objects have moved. It’s an early-stage, stealth robotics startup with a small founding team. The model is transformer-based and trained largely in simulation, on large GPU clusters in the cloud. This is an early hire to own distributed training end-to‑end — scaling from tens of GPUs to potentially thousands, with the freedom to challenge the existing stack and shape long‑term infrastructure decisions. A hands‑on IC seat, not a management one. What you'll do Own distributed training infrastructure and scale it across large GPU clusters Push training from tens of GPUs to hundreds, and eventually potentially thousands Improve GPU utilisation, throughput and end-to‑end training efficiency Optimise networking, storage, checkpointing and data pipelines for large jobs Profile and optimise across the full training stack Make long-term infrastructure and tooling decisions Potentially support cloud inference and deployment as the product develops What you’ll need Hands‑on experience training large transformer models across multi‑node GPU clusters Deep distributed‑training experience (PyTorch, NCCL, DeepSpeed, Megatron‑LM/FSDP) Cloud‑based GPU workloads, ideally AWS A track record improving utilisation, networking, storage, checkpointing and reliability Strong enough to lead this area independently and level up the existing team A hands‑on IC who still wants to write code Happy to work on‑site five days a week in London (visa sponsorship and relocation available) Bonus Large‑scale VLA or robotics model training Large‑scale computer vision transformer training Foundation model or LLM training infrastructure Ability to contribute to model architecture or transformer design Shortlisted candidates will be contacted within 48 hours.
RL Research Engineer
