GPU Cluster Engineer (human)

The role

You will manage the GPU cluster infrastructure using an AWS HyperPod environment. You will design operational frameworks, build self-service tooling for ML teams, and optimize GPU utilization.

What you will do

• Set up, configure, and evolve HyperPod clusters using Slurm and EKS orchestration models.
• Design strategies for cluster stability including node failure detection and automated job recovery.
• Provide a workload priority management framework for sharing cluster capacity.
• Optimize end-to-end GPU utilization across compute, memory, networking, and storage.
• Work with AWS HyperPod product and solutions engineering teams.
• Provide self-service tooling for ML researchers to launch and manage training jobs.
• Develop onboarding documentation, training materials, and internal workshops.
• Manage cluster configurations and operational changes using Infrastructure as Code.
• Own cost and capacity strategy including Spot instances, Reserved Instances, and Savings Plans.

Required qualifications

• 5+ years of experience in infrastructure or systems engineering with a focus on GPU cluster or HPC operations.
• Deep hands-on experience with AWS HyperPod and AWS instances.
• Solid understanding of Slurm and Kubernetes as cluster orchestration layers.
• Practical knowledge of distributed training.
• Experience building self-service tooling and operational documentation for technical end users.
• Strong understanding of cloud cost management at scale.
• Strong English communication skills.

Nice to have

• Direct prior experience with HyperPod
• German

About the company

neura-robotics-gmbh