Distinguished Engineer, Storage - AI Cloud

Responsibilities

• Lead the multi-year technical plan for AI Cloud Storage expansion across NCPs — determine the reference architecture, capabilities, performance and durability SLOs, qualification methodology, and roadmap for the high-performance file, object, and block storage that each NCP must offer to qualify for NVIDIA GPU…
• Serve as the chief storage architect with deep hands-on involvement.
• Lead key reviews of storage builds and investigate root causes of complex production problems.
• Develop prototype reference implementations to minimize risks in new initiatives. Make final technical decisions on NCP storage deliveries using measurable SLOs. Apply AI tools heavily to amplify your technical influence throughout the program.
• Define the standard for "production-ready" in NCP storage, including durability and availability SLOs measured in 9s. Ensure sustained efficiency per TiB, observability, blast-radius containment, and reduced operational toil. Influence GPU delivery gating by requiring AI Cloud to accept GPU capacity only after…
• Develop and guide the architectural direction by working closely with collaborators in training, inference, and accelerated-computing product lines. Coordinate with site-reliability, operations, networking, and security colleagues. Work together with external cloud providers, neocloud operators, and storage vendors to…
• Develop the open-source path forward for AI storage. Establish and guide an open-source strategy that broadens the AI storage ecosystem. Advocate for a GitHub-first, security-first stance. Engage deeply with upstream open-source communities. Formalize the APIs, SDKs, and protocols allowing partners and the industry to…
• Lead an engineering culture centered on AI tools. Regularly use modern AI coding and agentic tools in your daily tasks. Show what 10× engineering means at NVIDIA. Distribute patterns, prompts, and evaluation harnesses across the storage organization.
• Partner with peer Distinguished and Principal storage architects across the organization to tackle the most difficult, long-term technical challenges. Make automation the only acceptable solution for infrastructure management tasks like live software upgrades, node and drive replacements, capacity rebalancing, cross-DC…
• Design the storage layer for workloads spanning the next several GPU generations, including disaggregated inference with storage-backed KV caching, large-scale write-once-read-many inference patterns, exabyte regional object stores, and cross-DC dataset versioning and copy management.

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