Research Engineer / Scientist (Post-Training & RL)

Axiōma Search; Greater London; Híbrido; GBP 90.000 - 150.000; Jornada completa

Descripción de la vacante

Axiōma Search is building state-of-the-art foundation-model training infrastructure in London. This early hire will own distributed training end-to-end, scaling from tens to thousands of GPUs, and shape long-term infra decisions. You will stay hands-on, writing code while guiding the stack’s evolution.

You’ll work on-site five days a week in London, contributing to high-performance GPU clusters and cloud-based training pipelines as the product develops.

Formación

• Hands-on experience training large transformer models across multi-node GPU clusters.
• Deep distributed-training experience with PyTorch, NCCL, DeepSpeed, Megatron-LM/FSDP.
• Cloud-based GPU workloads, ideally AWS.
• Track record improving utilisation, networking, storage, checkpointing and reliability.
• Able to lead this area independently while contributing code.

Responsabilidades

• Own distributed training infrastructure and scale it across large GPU clusters.
• Push training from tens of GPUs to hundreds, and thousands.
• Improve GPU utilization, 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.

Conocimientos

Distributed training

PyTorch

NCCL

DeepSpeed

Megatron-LM

AWS

GPU utilization

On-site London

Descripción del empleo

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.