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# Senior Production Engineer, Managed Cloud

- **Company:** Crusoe's Inc
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $170,000.0 - $205,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Automation of Tests, Bash Shell, C++ (Programming Language), Cloud Computing, Distributed Systems, Fault Tolerance, Python (Programming Language), Performance Tuning, Cloud Services, Software Engineering, AI Infrastructure, Cloud Platform System, Large Language Models, AI Platforms, Kubernetes, Golang, Programming Languages
- **Published:** July 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=aa3974c17f302587

## About the Role

* Strong software engineering background - experience building production-grade systems beyond scripting or Bash
 * Demonstrated experience in distributed systems design and implementation
 * 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++)
 * Familiarity with Kubernetes or container orchestration platforms
 * Strong collaboration and communication skills
 * Ability to thrive in a fast-paced, mission-driven environment

## Description

At Crusoe, our Production Engineering team ensures the reliability and scalability of Crusoe's AI-optimized cloud platform. We're looking for a Senior Production Engineer with a strong background in distributed systems and cloud services to help us build and deliver a seamless Cloud experience 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 Be Working On

 * Design and operate reliable managed AI services with a focus on serving and scaling LLM workloads
 * Define, measure, and improve SLIs/SLOs across 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 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


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