GPU Cluster Engineer - Scalable AI Infrastructure

NEURA Robotics; Germany (OH); On-site; USD 140,000 - 195,000; Full time

Job summary

NEURA Robotics seeks a senior infrastructure engineer to own and evolve its large-scale GPU cluster platform on AWS HyperPod. You will design, operate, and optimize the infra that enables foundation model training and customer workloads.

You will implement Slurm and Kubernetes orchestration, build self-service tooling, automate reliability, and partner with ML researchers, product teams, and AWS to improve throughput, stability, and cost awareness across teams.

Qualifications

• 5+ years of experience in infrastructure or systems engineering.
• Strong experience with GPU cluster or HPC operations.
• Hands-on with AWS HyperPod and GPU instances.
• Solid understanding of Slurm and Kubernetes.
• Experience building self-service tooling and end-user docs.

Responsibilities

• Own the design, deployment, and operation of NEURA's GPU cluster platform on AWS HyperPod.
• Develop and refine orchestration for HyperPod/Slurm and HyperPod/EKS.
• Improve cluster stability: failure detection, automatic recovery, and fault-tolerant multi-node training.
• Build workload prioritization to fairly share cluster capacity across teams.
• Optimize end-to-end GPU utilization across compute, memory, networking, and storage.
• Collaborate with AWS HyperPod teams and internal orgs to influence roadmap.
• Deliver self-service tooling for researchers to launch and monitor training jobs.

Skills

GPU cluster operations

AWS HyperPod

Slurm

Kubernetes

Distributed training

Self-service tooling

Infrastructure documentation

Cost management

Cross-functional collaboration

English communication

German knowledge

Tools

Terraform

Job description

NEURA Robotics seeks a senior infrastructure engineer to own and evolve its large-scale GPU cluster platform on AWS HyperPod. You will design, operate, and optimize the infra that enables foundation model training and customer workloads.

You will implement Slurm and Kubernetes orchestration, build self-service tooling, automate reliability, and partner with ML researchers, product teams, and AWS to improve throughput, stability, and cost awareness across teams.