- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience.
- A minimum of 18+ years of practical engineering experience in storage technology is needed. This involves extensive involvement with a high-performance parallel file system like Lustre, GPFS / Spectrum Scale, WEKA, VAST, BeeGFS, DAOS, or its equivalent, handling data at multi-petabyte scale. Candidates must also have wide-ranging expertise in object storage (S3 / Swift-class) and block storage (NVMe-oF, NVMesh-class, iSCSI).
- A track record of crafting and managing storage platforms at exabyte scale for performance-critical workloads — AI training, HPC, video, or hyperscale data lakes — including direct responsibility for durability, availability, and performance SLOs measured in 9s.
- Demonstrated ability to set technical strategy across business units and partner organizations. You have driven multi-year storage architectures adopted by multiple teams, vendors, or customers. You can point to measurable outcomes such as GPU utility lift, $/PB reduction, incidents eliminated, and time-to-bring-up compressed.
- You are 100% hands-on in engineering. You write and review production code yourself. When a bug requires it, you read Lustre, NFS, kernel, NVMe-oF, or SPDK source code. You also run scale tests or recovery drills personally instead of delegating.
- Strong proficiency in at least one systems language (C, C++, Rust, or Go) and proficiency in Python; comfortable in the Linux kernel storage and networking stacks (block layer, RDMA / RoCE / InfiniBand, NVMe, page cache, VFS, multipath).
- Frequent daily use of advanced AI coding and autonomous tools, including specific examples showing how you accelerated building, coding, debugging, validation, and operations. Also, share your perspective on future trends.
- Excellent written and verbal communication. You can write a one-pager that aligns a VP. You can also write a six-pager that aligns an entire org. You can explain a deep technical trade-off to an SRE, a vendor CTO, and an internal customer in the same week.
- Comfort operating in a 24/7 production environment where storage incidents directly impact GPU revenue, with a security-first approach baked into every build.
Ways to stand out from the crowd
- Proven background in designing or managing storage solutions for AI training or inference at 10k+ GPU scale, demonstrating clear improvements in GPU utilization or reducing I/O bottlenecks.
- Open-source contributions or maintainership in Lustre, NFS, SPDK, NVMe / NVMe-oF, CSI, Ceph, MinIO, RocksDB, or related projects.
- Built or led a disaggregated-inference or Inference-Time-Compute storage architecture — KV caching to fast in-cluster or GPU-adjacent storage, WORM at scale, storage-aware scheduling, or database-integrated inference.
- Public technical contributions – patents, peer-reviewed papers (FAST, SOSP, NSDI, OSDI, ATC), keynote talks, or RFCs — that demonstrate expertise and leadership in storage for AI infrastructure.
NVIDIA led the way in accelerated computing. Today, our AI infrastructure drives global intelligence, changing industries worldwide. The AI Cloud Storage group forms the base that maintains the world's largest GPU fleet's productivity. Every model trained, every inference served, and every checkpoint saved passes through systems we develop, construct, and manage.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 320,000 USD – 488,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.