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# Senior Product Architect, Storage

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon S3, Databases, Network Congestion, Data as a Services, Data Infrastructure, Microprocessors, Ethernet, General Parallel File Systems, InfiniBand, NetApp Applications, Network Virtualization, Remote Direct Memory Access, AI Infrastructure, Data Processing, Graphics Processing Unit (GPU), Performance Testing, Retrieval-Augmented Generation, Large Language Models, Indexer, AI Platforms, Pure Storage, Storage Technologies, Information Technology, Deployment Automation
- **Published:** October 31, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Product-Architect--Storage_JR2014812-1

## About the Role

* 12+ years of experience architecting datacenter-scale AI, HPC, or storage infrastructure as a Principal Architect, Solutions Architect, Principal Engineer, or equivalent.
 * Bachelors in Computer Science or related field (or equivalent experience).
 * Deep expertise in AI infrastructure build, including disaggregated inference architectures, LLM training pipelines, and autonomous AI system patterns.
 * Hands-on experience with RDMA (RoCEv2/InfiniBand), high-performance storage protocols (NVMeoF, GPFS, Lustre, or S3-compatible object storage), and low-latency fabric design.
 * Strong understanding of KV Cache management strategies, including tiered memory/storage hierarchies for inference optimization.
 * Familiarity with Retrieval-Augmented Generation (RAG) architectures and the storage, indexing, and retrieval patterns they demand at scale.
 * Experience with NVIDIA DOCA or equivalent DPU/SmartNIC programming frameworks for offloading data plane and storage services.
 * Proven foundation in networking: Spectrum-X Ethernet, InfiniBand, NVLink Switch fabrics, congestion control, and datacenter topologies., * Proven experience designing reference architectures jointly with storage or infrastructure OEM partners (e.g., NetApp, DDN, VAST, Pure Storage, Dell or similar).
 * Hands-on deployment experience with disaggregated inference systems, including prefill/decode separation, KV Cache offload, and request routing.
 * Deep familiarity with NVIDIA Grace-Hopper, Grace-Blackwell, or upcoming Vera-Rubin platforms and their system-level implications for AI workloads.

## Description

As an AI Storage Platform Architect at NVIDIA, this position will be the linchpin between cutting-edge hardware platforms and real-world AI deployments - translating the capabilities of Rubin GPUs, Vera CPUs, BlueField DPUs, NVLink fabric, and Spectrum-X networking into validated, production-ready blueprints. Work hand-in-hand with storage ecosystem partners to co-develop reference architectures for the NVIDIA AI Data Platform and beyond, ensuring that every layer of the stack - compute, fabric, memory, and storage - is optimized for modern AI workloads!

What you'll be doing:

 * Architect end-to-end reference architectures for disaggregated inference (aligned with NVIDIA Dynamo), large-scale foundation model training, and agentic AI pipelines - co-developed with storage and ecosystem partners.
 * Design and validate storage-optimized AI infrastructure, including KV Cache tiering strategies, checkpoint acceleration, and high-throughput dataset pipelines that leverage RDMA and NVMeoF fabrics.
 * Define system-level architectures spanning Rubin graphics processors, Vera central processing units, BlueField data processing units, NVLink interconnects, and Spectrum-X Ethernet to improve efficiency across the full AI lifecycle.
 * Develop and publish reference architectures, whitepapers, and deployment guides for the NVIDIA AI Data Platform and partner-integrated solutions.
 * Drive prototyping, benchmarking, and performance validation of AI infrastructure at scale - diagnosing bottlenecks across compute, networking, and storage layers.
 * Leverage DOCA to architect DPU-offloaded data services including storage acceleration, telemetry, security enforcement, and network virtualization.
 * Collaborate with RAG and autonomous AI teams to build retrieval-optimized storage architectures, including vector database integration, low-latency object access patterns, and inference-aware caching.
 * Partner with customers and collaborators in the ecosystem to co-innovate, deliver proof-of-concepts (POCs) and MVPs that demonstrate end-to-end AI platform performance leadership.