As an AI Storage Platform Architect at NVIDIA, you will serve as a critical link between advanced hardware platforms and practical AI deployments, working closely with storage ecosystem partners to develop reference architectures that optimize compute, fabric, memory, and storage layers for modern AI workloads. Your daily tasks include architecting end-to-end solutions for disaggregated inference and large-scale foundation model training, designing optimized infrastructure using RDMA and NVMeoF fabrics, and validating system-level architectures across Rubin GPUs, Vera CPUs, BlueField DPUs, and Spectrum-X networking. You will also publish technical documentation and collaborate with RAG and autonomous AI teams to build retrieval-optimized storage systems. This role requires extensive experience in datacenter-scale AI infrastructure, deep expertise in high-performance storage protocols like NVMeoF and GPFS, and familiarity with NVIDIA DOCA for DPU-offloaded services. Ideal candidates have a background in computer science and hands-on deployment experience with disaggregated inference systems and NVIDIA’s latest hardware platforms.
Architect end-to-end reference architectures for disaggregated inference and large-scale foundation model training.
Design storage-optimized AI infrastructure with KV Cache tiering strategies and checkpoint acceleration.
Define system-level architectures using Rubin GPUs, Vera CPUs, BlueField DPUs, NVLink, and Spectrum-X networking.
Develop and publish deployment guides and whitepapers for the NVIDIA AI Data Platform and partner solutions.
Drive prototyping, benchmarking, and performance validation of AI infrastructure to diagnose bottlenecks.
What we're looking for
Over 12 years of experience as a Principal Architect in datacenter-scale AI infrastructure.
Bachelor’s degree in Computer Science or equivalent technical field.
Expertise in designing disaggregated inference architectures and LLM training pipelines.
Hands-on experience with RDMA, NVMeoF, GPFS, Lustre, and S3-compatible object storage.
Strong understanding of KV Cache management strategies for AI optimization.
Experience with NVIDIA DOCA or similar DPU programming frameworks for data services.
Proven track record in collaborating with OEM partners to design reference architectures.
Market check
Salary context
This
$224,000–$356,500
range sits above
97%
of similar postings on FindRole.
Peer median band
$152,000–$234,240
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$151,475–$235,750
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
802 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 798 roles with salary data.
Remote (Us, Ca, Santa Clara, US)14 days ago
$208,000–$327,750
Python
Kubernetes
Ansible
DevOps
Container Runtimes
AI workloads
RAG-based workflows
Inference at scale
Large scale training
Model evaluation
HPC
Data center deployments
PostgreSQL
MVPs
POCs
CI/CD
Newport Beach Ca-700, US17 days ago
$137,610–$168,190
PURE
NetApp
VMware
Brocade SAN fabric switches
AWS
Azure
Google Cloud
Hyper Converged Infrastructure
CI/CD
Linux
Windows
SAN/NAS architectures
Docker
Kubernetes