Infrastructure Engineer (ML Compute) - Paris

present of AI; Paris; Hybrid; EUR 65,000 - 90,000; Full time

Job summary

H Company seeks a GPU infra engineer to keep a fleet of GPUs healthy and efficiently placed for research and product workloads. The role requires running GPU workloads on Kubernetes, tracing faults through Linux, drivers, networking and storage, and writing solid Python.

SRE or infra engineering background is a bonus, while ML research experience is not required. The successful candidate will balance training and serving capacity, automate routine upkeep, and collaborate with cloud providers to

Qualifications

• Experience running GPU workloads on Kubernetes.
• Trace faults through Linux, drivers, networking and storage.
• Solid Python programming skills.
• Experience as an SRE or infrastructure engineer is a plus.

Responsibilities

• Keep a fleet of GPUs healthy, busy and well placed for training and serving models.
• Automate fault detection and remediation across the stack.
• Coordinate capacity trade-offs between training and inference workloads.
• Lend support during cloud-provider negotiations and optimization.

Skills

Kubernetes

Python

Linux troubleshooting

Incident management

Tools

Kubernetes

Job description

This role is about keeping a fleet of GPUs healthy, busy and well placed so research and product teams can train and serve models. A strong candidate has run GPU workloads on Kubernetes, can trace faults down through Linux, drivers, networking and storage, and writes solid Python. Experience as an SRE or infrastructure engineer helps, and ML research background is not required.

How it fits the AI era

AI models are the main customer here, so the work centers on feeding them reliable compute, and tools that detect and drain bad nodes can automate much of the routine upkeep. What stays human is the judgment: deciding how to trade capacity between training and serving, handling messy failures that cross hardware and software, and negotiating with cloud providers. Good infrastructure choices directly decide how fast a team can try new ideas.

How it builds AI experience

Worth showing

• GPU workloads on Kubernetes
• Linux and driver debugging
• Strong Python
• Incident handling and node repair

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About H Company