Infrastructure Engineer (ML Compute) - Paris

The role

You will own the GPU compute that powers model training, evaluation and serving systems. You will run clusters, GPUs and schedulers and manage capacity from cloud providers.

What you will do

• 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

Required qualifications

• 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

Nice to have

• worked as an SRE or infrastructure engineer
• AWS services such as EKS or SageMaker HyperPod
• Multi-cloud orchestration tools such as SkyPilot
• Kubernetes scheduling and autoscaling tools such as Kueue or Karpenter
• High-performance interconnects and collective communication libraries such as EFA, InfiniBand, NCCL or Open MPI
• Terraform
• Slurm

Benefits

• competitive package

Before you 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 En

About the company

hcompany's Infrastructure team works at the lowest layers of the stack, from Kubernetes down to the GPU hardware.