Infrastructure Engineer (ML Compute) - London
H Company· Engineering
Hybrid (Hybrid London), Hybrid (Hybrid Paris)Full-timePosted 1w ago
About the role
Role focused on owning and operating GPU compute for model training, evaluation and serving systems. Sits in the Infrastructure team, handling Kubernetes down to GPU hardware, cluster management, capacity planning with cloud providers, and reliability of GPU-based workloads.
Responsibilities
- 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
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
Skills
Required:GPU workloads on KubernetesDesigning GPU clusters for training/online RLOS-level debugging (Linux, GPU drivers, networking, storage)Python
Last updated Oct 15, 2026. Interested? Apply on the company site.
About H Company
H is an AI Research Lab and Product Company
H is an AI Research Lab and Product Company
AIProductResearchfrontier-labsheadline-ai-europe-100-2026liquid-ai-recruiting-target
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