Staff Software Engineer

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

Crusoe’s Data Center Infrastructure Engineering (DCIE) team is fundamental to our mission of providing AI hardware and infrastructure as a service. The team provides infrastructure for Crusoe’s fleet GPU’s and data center. The team sits at the nexus of high performance computing and AI infrastructure as a service.

The DCIE team owns, deployment maintenance, observability, critical environment, and automation. The team builds, and maintains GPU clusters, develops automation for logical and physical maintenance, provision systems, and observability tooling.

We are seeking a highly skilled and motivated Software Engineer to join Crusoe’s Data Center Infrastructure Engineering team. This position is focused on the development of software for the management of a fleet of GPU servers as well as the data centers that house those systems. The role focuses on the developing and implementing advanced diagnostic, observability, automation and repair tooling for high-performance GPU compute clusters.

The ideal new team member will be a hands-on problem solver who is comfortable working independently. The new team member will play a critical role in maintaining the health and scalability of Crusoe’s rapidly growing GPU fleet.

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  Developing and implementing deep-level diagnostics and troubleshooting of hardware faults within GPU racks and high-density compute systems.

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  Developing troubleshooting and automation tooling for GPU platforms including NVIDIA A100, H200, GB200, B200 and AMD 350X / 355X.

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  Developing automation and AI agents for executing component-level diagnosis and remediation for failed or degraded hardware.

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  In conjunction with data center operations develop innovative tooling and AI agents for managing the critical environment.

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  Developing tooling for post-repair validation and testing tools such as burn-in, Pytorch, and NVIDIA NCCL to ensure system stability and performance.

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  Own the deployment, monitoring, and operational support of developed tooling, ensuring solutions maximize GPU fleet availability and performance to drive customer success.

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  Developing automation and operational tooling for facilities management power as well as direct liquid cooling hardware systems

• Software engineering experience.

• The ability to identify a problem, rapidly develop a scalable solution and ship it.

• Ability to lean in and assist team members working on critical or complex technical initiatives.

• Ability to set the technical direction for a specific project and execute.

• Expertise in distributed systems, reliability, and cloud platforms (Kubernetes, IaC, GCP etc.)

• Strength in at least one programming language - Go, Python, Java, Rust.

• Strong analytical and problem-solving skills.

• Excellent communication and collaboration skills.

• Ability to work independently and within a team

• Experience with Temporal and Kubernetes.

• Experience working directly with hardware vendors.

• Background in large-scale GPU fleet operations or hyperscale data center environments.