Infrastructure Engineer (ML Systems) - London

Success is measured by how well we use our GPUs: keeping production reliable and improving the performance of our training and serving stack.

Running Kubernetes clusters for GPU training and inference

Improving how GPU work is scheduled and placed

Detecting, draining and repairing unhealthy GPU nodes

Managing capacity with our cloud providers

Debugging failures in GPUs, Linux, storage and networking

By month 3, you’ll be handling a share of cluster incidents on your own and will have shipped one change to scheduling or node health, such as automatically detecting and draining unhealthy GPU nodes. By month 6, you’ll own one part of the compute layer outright: scheduling and placement, node health and repair, or capacity and new regions. By month 12, you’ll be responsible for a globally distributed GPU system that shifts capacity between training and inference as needs change.

Your hiring manager is our VP of Engineering. Day to day, you’ll work with the Platform Infrastructure team, the research and product teams who use our compute, and our cloud providers and other vendors.

Have built and run GPU workloads on Kubernetes

Could design a GPU cluster for training and online RL from scratch

Debug confidently at the OS layer and below (Linux, GPU drivers, networking and storage)

Write strong Python

Slurm

Kubernetes scheduling and autoscaling tools such as Kueue or Karpenter

Multi-cloud orchestration tools such as SkyPilot

AWS services such as EKS or SageMaker HyperPod

High-performance interconnects and collective communication libraries such as EFA, InfiniBand, NCCL or Open MPI

Terraform

You don’t need ML research experience. If you match most of this but not every point, please apply.

Four stages, about three hours in total: a 30-minute call with our Talent team, a 60-minute coding interview, a 60-minute system design interview and a closing 30-minute conversation with our VP of Engineering. The coding and system design interviews are modelled on real-world problems you’d face in this role.

The role is hybrid, with three days a week in our Paris or London office and occasional travel between the two. We offer a competitive package.