Infrastructure Engineer (ML Compute) - London
H Company · Hybrid London · Hybrid Paris
CHECKED 52M AGO
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At a glance
Highlights
- Own the GPU compute powering model training, evaluation and serving.
- Improve GPU utilization and reliability across training and serving stacks.
- Build a global system shifting capacity between training and inference.
Ideal candidate
Low-level infrastructure engineer who thrives owning production compute and squeezing performance from GPUs. You love debugging hardware-to-OS failures and keeping critical systems reliable for researchers.
Requirements
- Built and run GPU workloads on Kubernetes
- Able to design a GPU cluster for training and online RL from scratch
- Confident debugging at OS layer and below across Linux, drivers, networking and storage
- Strong Python skills
- remote policy
- Hybrid, 3 days/week in London or Paris office
- hiring process
- Four stages (~3 hours total): 30-min talent call, 60-min coding interview, 60-min system design interview, 30-min conversation with VP of Engineering.
Lab profile
About H Company
Pushing superintelligence boundaries with autonomous agentic AI that automates complex multi-step tasks.
What they build
- Autonomous agentic AI systems
- Foundational infrastructure for autonomous AI
Known for
- Post-training
- Model inference and serving
- AI infrastructure
- Safe and responsible AI development
Key facts
- Teams in Paris, London, and the US