NVIDIA
Senior Storage Production Engineer - DGX Cloud
$176K–$334K
US, CA, Santa Clara +1 · Senior · Full-time · Posted Jun 14, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 8+ years of practical experience.
- Education
- BS degree or equivalent experience in Computer Science, Storage Systems, or a related technical field.
- Visa sponsorship
-
About the job
Designs, operates, and continuously improves large-scale storage systems for NVIDIA’s GPU cloud services and AI/ML workloads, with a focus on reliability, performance, automation, and data integrity.
What you'll do
- Design, implement, and support scalable, highly available storage clusters and their monitoring, logging, and alerting systems.
- Optimize storage architecture and efficiency for AI/ML workloads, including caching, data placement, tiering, and performance.
- Manage storage service lifecycle and production health, including capacity planning, incident response, and on-call support.
What they ask for
- BS degree or equivalent experience in Computer Science, Storage Systems, or a related technical field.
- Experience with distributed and high-performance storage solutions and block, file, and object storage technologies.
- Experience with storage networking protocols such as NFS, SMB, iSCSI, S3, Fibre Channel, RDMA, and NVMe over Fabrics.
- Expertise in algorithms, data structures, software design, and automating Linux-based storage systems; experience in one or more listed programming languages and configuration-management tools.
Nice to have
- Deep understanding of distributed storage systems, replication, and erasure coding; experience in capacity planning and performance troubleshooting.
- Experience with infrastructure as code, CI/CD, cloud storage, Kubernetes, OpenStack, or automated storage migration, backup, and disaster recovery.
Pay
176,000 USD - 276,000 USD base salary (Level 4); 208,000 USD - 333,500 USD base salary (Level 5)
Tools
NFS, SMB, iSCSI, S3, Fibre Channel, RDMA, NVMe over Fabrics, Linux
AI focus
Improve storage architectures for AI/ML workloads; use predictive analytics and AI-driven automation to monitor and maintain production storage; scale systems with AI/ML-driven automation.
Model-written summary · the original posting is authoritative