Senior Solutions Architect, AI Infrastructure

Nvidia

Confirmed live yesterday Trusted

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 71 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $199k

This role $236k

$147k most similar roles pay here $303k

This role pays more than 79% of similar roles. Most pay $162,000–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $199k for comparable roles.

Based on 239 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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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View all roles at Nvidia

At a glance

TL;DR · Senior Solutions Architect, AI Infrastructure

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Senior Solutions Architect - AI Infrastructure will join the NVIDIA Cloud Partners team to focus on GPU, NVLink, and infrastructure design. This role serves as a technical expert bridging engineering and field teams to support customers with demanding requirements for next-generation GPU-based clusters. You will be responsible for end-to-end cluster design, performance modeling, validation, and NPI cluster deployments while providing guidance on networking architectures and topologies. The position requires expertise in large-scale distributed systems, AI clusters, and HPC infrastructure. Key technical skills include experience with NVLink, NVIDIA Networking, NCCL, MPI, IMEX, NMX, and collectives in distributed training. You will translate complex engineering concepts into customer-ready documentation while debugging issues related to cluster configuration and performance. The role addresses the challenge of building high-performance enterprise AI infrastructure and advanced supercomputers for cloud providers and research institutions.

What does a Solutions Architect earn in California?

Median $235750 from 45 postings across 8 companies.

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Skills

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

What you'll do

Design and architect large-scale GPU clusters for advanced AI supercomputers and enterprise infrastructure.
Provide technical guidance to cloud partners on optimal cluster designs while navigating complex situational limitations.
Perform performance modeling, validation, and end-to-end architecture design for next-generation GPU systems.
Manage first deployments of new products, including innovative network architectures and topologies.
Translate complex engineering concepts into customer-ready documentation, diagrams, and reference materials.
Debug cluster design, configuration, and performance issues using internal engineering expertise.
Relay customer and field feedback regarding cluster designs and workflows back to internal engineering teams.
Drive technical issues to resolution by coordinating between product, support, and engineering teams.

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 for 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 materials.
Expertise in driving customer and partner issues to resolution with product and engineering teams.
Ability to manage multi-functional communications across customers, product teams, support teams, and engineering teams.
Experience leading large-scale AI Factory or HPC cluster bring-ups and builds.
Hands-on experience with NVIDIA products including GPUs, NVLink, and Networking for debugging deployment issues.

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