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You will own the GPU compute infrastructure that powers model training, evaluation, and serving systems. This role sits within the Infrastructure team, working at the lowest layers of the stack from Kubernetes down to GPU hardware. You'll manage the clusters, GPUs, and schedulers that research, product, and platform teams depend on, and handle capacity management with cloud providers.
Key responsibilities include:
- Running and maintaining Kubernetes clusters for GPU training and inference workloads
- Improving GPU work scheduling and placement algorithms
- Detecting, draining, and repairing unhealthy GPU nodes
- Managing compute capacity with cloud providers
- Debugging failures across GPUs, Linux, storage, and networking layers
Success is measured by GPU utilization efficiency, production reliability, and performance improvements to the training and serving stack.
First-year milestones: By month 3, you'll handle cluster incidents independently and ship scheduling or node health improvements. By month 6, you'll own one compute layer component (scheduling/placement, node health/repair, or capacity/regions). By month 12, you'll manage a globally distributed GPU system that dynamically shifts capacity between training and inference.
You'll report to the VP of Engineering and collaborate daily with the Platform Infrastructure team, research and product teams, and external cloud providers and vendors.
Requirements:
- Built and run GPU workloads on Kubernetes
- Can design a GPU cluster for training and online RL from scratch
- Debug confidently at OS layer and below (Linux, GPU drivers, networking, storage)
- Strong Python programming skills
Plus factors (not required):
- SRE or infrastructure engineering background
- Experience with Slurm, Kubernetes scheduling tools (Kueue, Karpenter), or multi-cloud orchestration (SkyPilot)
- AWS services: EKS, SageMaker HyperPod
- High-performance interconnects: EFA, InfiniBand, NCCL, Open MPI
- Terraform
ML research experience is not required. The role is hybrid with three days per week in the Paris or London office and occasional travel between locations.
