Senior Solutions Architect, AI Infrastructure

Nvidia

Confirmed live today Trusted

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CACANY
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 117 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $197k

This role $236k

$138k $304k

below market most similar roles pay here above market

This role pays more than 84% of similar roles. Most pay $158,750–$235,750 — the blue band above. At the midpoint, this role pays about $236k versus about $197k for comparable roles.

Based on 240 similar postings.

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 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Solutions Architect, AI Infrastructure

Role Posting Log in to save

The Senior Solutions Architect - AI Infrastructure joins the NVIDIA Cloud Partners team to lead the design and architecture of next-generation GPU-based clusters for AI supercomputers and enterprise infrastructure. This role involves performing end-to-end cluster design, performance modeling, validation, and NPI cluster deployments while bridging engineering and field teams. You will guide partners through complex design principles, debug configuration and performance issues, and provide feedback on cluster workflows. Key technical requirements include expertise in GPU, NVLink, and networking architectures, as well as experience with NCCL, MPI, IMEX, NMX, and collectives in distributed training. The position requires a deep understanding of large-scale distributed systems and HPC infrastructure to solve demanding requirements for cloud providers, hyperscalers, and research institutions building advanced AI factories.

What does a Solutions Architect earn in California?

Median $235750 from 57 postings across 11 companies.

See salary data

Skills

GPU NVLink HPC Distributed Systems NVIDIA Networking NCCL MPI IMEX NMX Cluster Design Performance Modeling NPI AI Infrastructure Distributed Training

What you'll do

Design end-to-end architectures for large-scale GPU-based clusters and enterprise AI infrastructure.
Provide technical guidance to cloud partners on cluster design, networking topologies, and performance optimization.
Perform performance modeling and validation for next-generation GPU and NVLink technologies.
Support New Product Introduction (NPI) deployments for customers using new GPU and networking architectures.
Debug complex issues related to cluster design, configuration, and performance using internal engineering expertise.
Translate customer and field feedback regarding cluster workflows back to internal engineering teams.
Create customer-ready documentation, diagrams, and reference materials for sophisticated engineering concepts.

What we're looking for

BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, Physics, or a related field (or equivalent experience).
8+ years of experience in cluster design, validation, and issue resolution, specifically on GPU and HPC clusters.
Proven expertise in designing large-scale distributed systems, AI clusters, or HPC infrastructure.
Ability to translate sophisticated engineering concepts into customer-ready documentation, diagrams, and reference material.
Expertise in driving customer and partner issues to a close with product and engineering teams.
Ability to handle multi-functional communications across customer, product, support, and engineering teams.
Experience leading large-scale AI Factory or HPC cluster bring-ups or builds (preferred).
Hands-on experience with NVIDIA products, including GPUs, NVLink, and NVIDIA Networking (preferred).

Save