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.

About the Role:
At Crusoe, our Site Reliability Engineering team ensures the reliability and scalability of Crusoe’s AI-optimized cloud platform. We’re looking for a Staff Site Reliability Engineer with a strong background in distributed systems and hands-on experience with large language models to help us build and operate managed AI services at scale. This role is central to delivering highly available, performant, and cost-efficient AI infrastructure that powers compute-intensive, latency-sensitive workloads for our customers.

What You’ll Work On:

• Design and operate reliable managed AI services with a focus on serving and scaling LLM workloads

• Build automation and reliability tooling to support distributed AI pipelines and inference services

• Define, measure, and improve SLIs/SLOs across AI workloads to ensure performance and reliability targets are met

• Collaborate with AI, platform, and infrastructure teams to optimize large-scale training and inference clusters

• Automate observability by building telemetry and performance tuning strategies for latency-sensitive AI services

• Investigate and resolve reliability issues in distributed AI systems using telemetry, logs, and profiling

• Contribute to the architecture of next-generation distributed systems purpose-built for AI-first environments

What You’ll Bring:

• Strong software engineering background — experience building production-grade systems beyond scripting or Bash

• Demonstrated experience in distributed systems design and implementation

• Hands-on work with large language models (LLMs) or AI/ML infrastructure

• SRE mindset and experience (whether or not under the SRE title) including:

  • Defining and measuring SLIs/SLOs

  • Building monitoring and observability systems

  • Driving performance and reliability improvements

  • Designing fault-tolerant systems and automated testing strategies

• Proficiency in at least one modern programming language (Python, Go, Java, C++)

• Deep working knowledge of Kubernetes (cluster architecture, networking, scheduling, and workload management)

• Strong collaboration and communication skills

• Ability to thrive in a fast-paced, mission-driven environment

Bonus Points:

• Experience scaling inference or training workloads for LLMs

Benefits:

Crusoe also offers a competitive benefits package designed to support financial security, health, and overall well-being. Our benefits are tailored to local market standards and include core offerings such as pension contributions and additional perks to support work-life balance.

Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.