Infrastructure Engineer (ML Compute) - Paris
H Company · Hybrid Paris · Hybrid London
CHECKED 52M AGO
Frontier labremote
At a glance
Highlights
- Own GPU compute powering model training, evaluation and serving systems.
- Build globally distributed GPU system balancing training and inference needs.
- Improve performance of training and serving stack while keeping production reliable.
Ideal candidate
Infrastructure-minded engineer who loves running large GPU fleets reliably and squeezing performance from low-level systems. Comfortable owning production and partnering with research teams, without needing ML research background.
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, GPU drivers, networking and storage
- Strong Python skills
- remote policy
- Hybrid, 3 days per week in Paris or London 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