Senior Solutions Architect, AI Infrastructure Build Readiness - Execution

Nvidia

Confirmed live today Trusted

Remote

Quick summary

Work type: Remote
Location: CATX
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 15 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 $198k

This role $236k

$138k $304k

below market most similar roles pay here above market

This role pays more than 85% of similar roles. Most pay $159,850–$235,750 — the blue band above. At the midpoint, this role pays about $236k versus about $198k 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.

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At a glance

TL;DR · Senior Solutions Architect, AI Infrastructure Build Readiness - Execution

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The Senior Solutions Architect, AI Infrastructure Build Readiness - Execution joins the AI Infrastructure Build Readiness - Execution team to drive technical engagement with OEM and deployment partners. This role involves mapping complex customer workloads to the hardware and software ecosystem, co-designing scalable architectures, and defining integration milestones. You will lead technical engagements to troubleshoot issues, drive platform adoption, and manage AI Factory build execution. Key responsibilities include identifying gaps between partner needs and offerings to shape internal engineering priorities. Required expertise includes high-performance computing, AI/ML, data center infrastructure, rack-scale AI infrastructure, and large-scale GPU systems. You must demonstrate proficiency with NVIDIA GPU, networking, and AI infrastructure platforms while utilizing skills in software/hardware engineering, developer relations, and solutions architecture to solve large-scale deployment challenges.

What does a Solutions Architect earn in California?

Median $235750 from 57 postings across 11 companies.

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Skills

AI Infrastructure High-Performance Computing GPU Systems Data Center Infrastructure Accelerated Computing AI/ML Rack-scale AI Infrastructure NVIDIA SDKs HPC Deployment Validation Hardware Engineering Networking

What you'll do

Map complex customer workloads to NVIDIA's hardware and software technology stack to achieve measurable outcomes.
Co-design advanced, scalable architectures with OEM and deployment partners, defining technical directions and integration milestones.
Design, validate, and support technical solutions that enable customer and partner success.
Identify gaps between partner needs and NVIDIA offerings to shape internal product and engineering priorities.
Lead technical engagements to help customers troubleshoot and resolve complex infrastructure issues.
Drive platform adoption by helping developers and enterprises integrate NVIDIA SDKs, frameworks, and systems.
Lead AI Factory build execution engagements, driving technical readiness and deployment validation for large-scale infrastructure.

What we're looking for

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience.
8+ years of professional experience in software/hardware engineering, developer relations, technical partnerships, solutions architecture, or product management.
3-5+ years of direct, hands-on experience in AI infrastructure, data center deployment, or large-scale AI/HPC systems.
Deep technical expertise in high-performance computing, AI/ML, data center infrastructure, rack-scale AI infrastructure, or large-scale GPU systems.
Experience working with OEM, ODM, system integrator, or deployment partner ecosystems to drive technical field engagement or platform adoption.
Ability to communicate complex technical ideas clearly to both technical and non-technical audiences, from engineers to C-level executives.
Travel up to 50% to support field engagements, partner sites, and deployment activities.
Experience contributing to deployment playbooks, execution frameworks, or readiness programs for large-scale AI infrastructure build-outs (preferred).

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