GPU Infra Engineer for ML Systems (Kubernetes)

Emploive; Paris; Hybrid; EUR 90,000 - 120,000; Full time

Job summary

Emploive is seeking a Platform Infrastructure Engineer to own the GPU compute powering model training, evaluation, and serving systems. You’ll run GPU clusters and Kubernetes to support research, product and platform teams, and manage capacity with cloud providers.

You’ll focus on reliability and performance of the training and serving stack, including scheduling, node health, and international capacity across regions. The role is hybrid with Paris/London offices and travel as needed.

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

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

Skills

GPU workloads on Kubernetes

Cluster design for training/RL

OS-level debugging

Python programming

SRE/Infrastructure experience

Tools

Kubernetes

Slurm

Kueue

Karpenter

SkyPilot

AWS EKS

SageMaker HyperPod

NCCL

Open MPI

Terraform

Job description

Emploive is seeking a Platform Infrastructure Engineer to own the GPU compute powering model training, evaluation, and serving systems. You’ll run GPU clusters and Kubernetes to support research, product and platform teams, and manage capacity with cloud providers.

You’ll focus on reliability and performance of the training and serving stack, including scheduling, node health, and international capacity across regions. The role is hybrid with Paris/London offices and travel as needed.