Senior GPU Infrastructure & Automation Engineer

Crusoe; San Francisco (CA); On-site; USD 250,000 - 300,000; Full time

Benefits offered by this job

Industry competitive pay

Restricted Stock Units

Health insurance

401(k) with match

Parental leave

Life insurance

Tuition reimbursement

Cell phone reimbursement

Bonus

Equity

Job summary

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

The role requires hands-on problem solving, independence, and ensuring health and scalability of Crusoe’s rapidly growing GPU fleet.

Qualifications

• Proven software engineering experience.
• Ability to identify problems and ship scalable solutions.
• Strong in distributed systems and cloud platforms (Kubernetes, IaC, GCP).
• Proficiency in at least one of Go, Python, Java, Rust.

Responsibilities

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

Skills

Go

Python

Java

Rust

Kubernetes

IaC

GCP

Distributed systems

Reliability

Tools

Kubernetes

Temporal

NVIDIA NCCL

GPU fleet tooling

Job description

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

The role requires hands-on problem solving, independence, and ensuring health and scalability of Crusoe’s rapidly growing GPU fleet.