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Senior Product Architect, Storage

NVIDIA· Architect
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Santa Clara, CA, Hybrid (Remote - US)$224k - $356.5kfull-timePosted 7mo ago

About the role

Architect end-to-end reference architectures for disaggregated AI storage infrastructures, translating Rubin GPUs, Vera CPUs, BlueField DPUs, NVLink and Spectrum-X networking into production-ready blueprints for NVIDIA AI Data Platform and beyond.

Responsibilities

  • Architect end-to-end reference architectures for disaggregated inference, 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 leveraging RDMA and NVMeoF fabrics.
  • Define system-level architectures spanning Rubin GPUs, Vera CPUs, BlueField DPUs, 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 POCs and MVPs that demonstrate end-to-end AI platform performance leadership.

Qualifications

  • 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.

Skills

Required:Architecting datacenter-scale AI/storage infrastructureRDMA and high-performance storage protocolsKV Cache management and tiered memory/storageRetrieval-Augmented Generation (RAG)Networking and datacenter fabrics
Nice to have:NVIDIA DOCA / DPU programming

Benefits

  • Equity and benefits
Last updated Jul 3, 2026. Interested? Apply on the company site.

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