Senior Storage Engineer
Lambda - San Francisco Office (Fremont St), San Francisco, California, United States, San Jose Office (Zanker)
Posted May 30, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
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- Verification
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- Salary
- Not verified
- 401(k) match
- Not verified not verified - source URL not recorded; timestamp not recorded
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Market context
- Median wage (BLS OEWS)
- $111,944 national median
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Application
- Cover letter
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- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Senior Storage Engineer San Francisco Office (Fremont St), San Francisco, California, United States, San Jose Office (Zanker) Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. *Note: This position requires presence in our San Francisco or San Jose office location 4 days per week; Lambda's designated work from home day is currently Tuesday. Product Engineering at Lambda is responsible for building and scaling our cloud offering. Our scope includes the Lambda website, cloud APIs and systems as well as internal tooling for system deployment, management and maintenance. For distributed AI workloads, GPU compute power is only one factor. High-performance networking and storage are essential for interconnecting these systems and supporting AI training and inference at scale. Lambda's Infrastructure Engineering team integrates advanced storage, networking, and compute hardware to build high-performance clusters. Our expertise lies at the intersection of: - High-Performance Distributed Storage Solutions: We deploy and maintain the storage systems that provide customer training and inference datasets at the speeds demanded by modern clustered GPUs. - Software Defined Networking: We deploy software defined network overlays that provide multi-tenant security and intelligent routing without compromising performance, using the latest in high-performance networking hardware. - Compute Virtualization: We enable virtualization that
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