Crusoe
Staff Software Engineer (Cloud Infrastructure)
San Francisco, CA - US · Staff+
Sponsorship not specified$215k-$260kDetected 8 days ago
GoCloud PlatformsLinuxElectrical EngineeringCommunicationCollaborationProblem Solving
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Lottery odds (Level IV)94
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About the role
- We are seeking a highly skilled and motivated GPU Fleet Operations Engineer to join Crusoe's Fleet Operations team.
- This role is focused on the advanced diagnosis, maintenance, and repair of high-performance GPU compute clusters, ensuring maximum uptime, reliability, and performance across our fleet.
- This position plays a critical role in maintaining the health and scalability of Crusoe's rapidly growing GPU fleet.
Responsibilities
- Develop software to troubleshoot and support GPU platforms including NVIDIA A100, H200, GB200, B200, B300 and AMD 350X / 355X.
- Partner with data center operations to manage and perform field-replaceable unit (FRU) repairs for GPUs, power supplies, cooling systems, interconnects, and networking hardware.
- Implement and execute preventative maintenance procedures to improve fleet reliability and extend hardware lifespan.
- Perform firmware and BIOS upgrades across the GPU fleet.
- Maintain detailed documentation of maintenance activities, failures, and resolutions in ticketing and asset management systems.
- Develop and update standard operating procedures (SOPs) for troubleshooting, repair, and validation workflows.
- Collaborate with engineering, software, and data center operations teams to identify root causes of systemic failures and implement preventative solutions.
Requirements
- Deep understanding of GPU architectures and hands-on experience with GPU-based systems.
- Proficiency with GPU and system diagnostic tools such as NVIDIA DCGM and NVIDIA field diagnostic utilities.
- Ability to code in Golang
- Familiarity with high-speed interconnects such as InfiniBand, NVLink, and RDMA over Converged Ethernet (RoCE).
- Ability to work independently in a fast-paced data center or operations environment.
Nice to have
- Technical certification or Associate's/Bachelor's degree in Electrical Engineering, Computer Science, or a related field or demonstrated experience.
- Experience working directly with hardware vendors and escalations.
- Background in large-scale GPU fleet operations or hyperscale data center environments.
Skills
- Crusoe is on a mission to accelerate the abundance of energy and intelligence.
- The demand for AI compute is boundless, and power is a bottleneck.
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration skills.
Compensation
- Compensation will be paid in the range of $215,000 - $260,000.
- Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data.
- 401(k) with a 100% match up to 4% of salary
Benefits
- Restricted Stock Units in a fast growing, well-funded technology company
- Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents
- Employer contributions to HSA accounts
- Paid Parental Leave
- Paid life insurance, short-term and long-term disability
- Generous paid time off and holiday schedule
- Cell phone reimbursement
- Tuition reimbursement
Company info
- Proven experience diagnosing and repairing high-density, rack-mounted compute hardware in production environments.
- Experience supporting NVIDIA A100, H200, GB200, B200 and AMD 350X / 355X series platforms.
- Strong Linux experience (Ubuntu, Rocky Linux, CentOS) using the command line for diagnostics and testing.
- Experience working with enterprise server hardware, power delivery, and cooling systems.
Equal opportunity
- Crusoe is an Equal Opportunity Employer.
Visa & Work Authorization
- 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 st
This listing is sourced directly from Crusoe's careers page and normalized into a canonical job model.