Research Engineer

Axiōma Search; Greater London; Sur place; GBP 108 000 - 180 000; Plein temps

Résumé du poste

A London-based robotics startup is seeking a Research Engineer to own the scaling of training infrastructure, improving speed and compute efficiency. You will work closely with researchers and tackle distributed systems challenges in a hands-on, early-stage environment.

Strong Python/PyTorch skills and experience with multi-node training are essential, with exposure to NCCL, FSDP or DeepSpeed and AWS cloud GPUs preferred. The role emphasizes ownership and fast learning.

Qualifications

• Strong Python and PyTorch experience with ML infrastructure focus.
• Experience scaling training to multi-node GPU clusters.
• Knowledge of distributed training tech (NCCL, FSDP or DeepSpeed).
• Experience with cloud GPUs, preferably AWS.

Responsabilités

• Build and operate multi-node GPU training infrastructure, including orchestration, scheduling, environments and recovery.
• Improve training reliability to reduce downtime from days to minutes.
• Profile end-to-end and remove throughput bottlenecks.
• Address checkpointing, data loading, storage, networking and image builds.
• Develop observability to track utilisation, performance and cost.
• Collaborate with researchers and allocate compute budget decisions.

Connaissances

Python

PyTorch

ML Infra

Multi-node

NCCL

FSDP

DeepSpeed

AWS

Debugging

Outils

CUDA

Triton

Description du poste

Up to £180k base + equity package

About

A VC-backed robotics startup in London is building a transformer-based foundation model for robotic manipulation. The team is fewer than ten people, technically ambitious and highly hands-on, with most training data generated through simulation.

Their next challenge is scaling training from the current cloud setup to hundreds of GPUs. That introduces a different class of engineering problems: distributed communication across nodes, reliable checkpointing, high-throughput storage and data loading, GPU utilisation, observability and compute cost.

They are looking for a Research Engineer to take ownership of this scaling work, improving both training speed and compute efficiency. You do not need to have solved every problem at this scale already. Strong ML infrastructure foundations, an understanding of how distributed systems begin to fail under load, and the appetite to learn quickly in an early-stage environment matter more than extensive experience.

What you'll do

• Build and operate the infrastructure behind multi-node GPU training, including job orchestration, scheduling, environments and recovery
• Make training reliable, so a failed node costs minutes rather than days
• Profile the stack end to end and remove the constraints limiting throughput
• Solve the less glamorous but critical problems across checkpointing, data loading, storage, networking and image builds
• Build the observability the team currently lacks, making utilisation, performance and cost visible
• Work directly with researchers and take infrastructure problems off their plate
• Grow into decisions about how a large compute budget is allocated and committed

What you'll need

• Strong Python and PyTorch
• Experience building ML infrastructure, with some exposure to multi-node GPU training
• A working understanding of distributed training, including NCCL, FSDP or DeepSpeed, and where these systems tend to break
• Experience working with cloud GPUs, ideally on AWS
• Willingness to debug below the orchestration layer across communications, storage, I/O and hardware
• A genuine appetite for the pace, ownership and ambiguity of an early-stage startup

Bonus

• Experience supporting researchers or research engineers directly
• Kernel or performance work using CUDA or Triton
• Deeper expertise in storage or networking
• Open-source contributions to training or inference infrastructure

Shortlisted candidates will be contacted within 48 hours.