Senior GPU Infra Engineer - Automation & Diagnostics

Crusoe; United States; On-site; USD 250,000 - 300,000; Full time

Benefits offered by this job

Industry competitive pay

RSUs in a fast-growing tech company

Health insurance with family options

401(k) with 100% match up to 4%

Paid Parental Leave

Paid life insurance & disability

Job summary

Crusoe is hiring a Software Engineer to join the Data Center Infrastructure Engineering team, focusing on software for managing a fleet of GPU servers and their data centers. You will develop advanced diagnostics, observability, automation, and repair tooling for high-performance GPU compute clusters.

The role requires hands-on problem solving, independence, and contribution to scalable health of Crusoe’s growing GPU fleet across energy‑efficient AI infrastructure.

Qualifications

• Proven software engineering experience building scalable systems.
• Experience with GPU compute or data center infrastructure is a plus.
• Strong problem-solving and collaboration skills.

Responsibilities

• Develop deep-level diagnostics and troubleshooting for GPU racks and high-density compute systems.
• Build automation tooling for GPU platforms (NVIDIA A100/H200/GB200/B200 and AMD 350X/355X).
• Create AI agents for diagnosis and remediation of hardware faults.
• Collaborate with data center operations to manage critical environment tooling.
• Develop post-repair validation and testing tooling (burn-in, PyTorch, NCCL).
• Own deployment, monitoring, and support of tooling to maximize GPU fleet availability.
• Develop automation for facilities management power and liquid cooling systems.

Skills

Go

Python

Java

Rust

Kubernetes

GCP

IaC

Distributed systems

Cloud

Tools

Git

CI/CD

Job description

Crusoe is hiring a Software Engineer to join the Data Center Infrastructure Engineering team, focusing on software for managing a fleet of GPU servers and their data centers. You will develop advanced diagnostics, observability, automation, and repair tooling for high-performance GPU compute clusters.

The role requires hands-on problem solving, independence, and contribution to scalable health of Crusoe’s growing GPU fleet across energy‑efficient AI infrastructure.