Senior Production Engineer, Managed AI
Crusoe is an AI infrastructure and cloud computing company. It provides GPU cloud capacity, managed AI services, inference, fine-tuning, data centers, and energy infrastructure for AI developers and enterprise customers.
Maintainer signals as of 8/23/2026
Funding history
Projects
About Crusoe, Inc
Crusoe designs, builds, and operates energy-first AI infrastructure, including data centers, GPU cloud computing, and modular AI factories. Crusoe Cloud provides GPU clusters, managed Kubernetes and Slurm, storage, networking, observability, managed inference, serverless fine-tuning, and model deployment through Crusoe Intelligence Foundry. Its customers include AI startups, enterprises, and organizations developing training, inference, analytics, and other compute-intensive workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design and operate reliable managed AI services for large language model workloads. Build automation, reliability tooling, telemetry, and observability systems; define and improve SLIs and SLOs; optimize training and inference clusters; investigate distributed-system issues; and contribute to AI-focused distributed systems architecture.
Requirements
- Strong software engineering background
- Experience building production-grade systems beyond scripting or Bash
- Experience designing and implementing distributed systems
- Hands-on experience with large language models or AI/ML infrastructure
- Experience defining and measuring SLIs and SLOs
- Experience building monitoring and observability systems
- Experience driving performance and reliability improvements
- Experience designing fault-tolerant systems and automated testing strategies
- Proficiency in Python, Go, Java, or C++
- Familiarity with Kubernetes or container orchestration platforms
- Strong collaboration and communication skills
- Ability to thrive in a fast-paced, mission-driven environment
- Experience scaling LLM inference or training workloads is a bonus
Responsibilities
- Design and operate managed AI services
- Build automation and reliability tooling for distributed AI pipelines and inference services
- Define and improve SLIs and SLOs across AI workloads
- Optimize large-scale training and inference clusters
- Build telemetry and performance-tuning strategies
- Investigate and resolve reliability issues
- Contribute to distributed systems architecture
Benefits
- Industry competitive pay
- Restricted Stock Units
- Health insurance options including HDHP and PPO
- Vision insurance
- Dental insurance
- Employer HSA contributions
- Paid parental leave
- Paid life insurance
- Short-term and long-term disability insurance
- Teladoc
- 401(k) with 100% match up to 4% of salary
- Paid time off
- Paid holidays
- Cell phone reimbursement
- Tuition reimbursement
- Calm app subscription
- MetLife Legal
- Company-paid commuter benefit of $300 per month
