NVIDIA
Senior Solutions Architect - Data Center Infrastructure
$152K–$288K
US, CA, Santa Clara +2 · Senior · Full-time · Posted Oct 27, 2026
- Location
- US, CA, Santa Clara
US, TX, Remote
US, WA, Seattle - Experience
- 4+ years of work-related experience in the high-tech electronics industry, particularly in system design or technical customer support roles.
- Education
- BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.
About the job
Architects and supports large-scale customer deployments of NVIDIA data center infrastructure, working across engineering, product, and sales to resolve technical issues and enable adoption.
What you'll do
- Design and scale distributed AI infrastructure, and develop technical demos and system designs.
- Provide technical and onsite support for GPU, networking, hardware, software, and deep learning inference issues.
- Analyze deployment data and logs, guide customers through technical roadblocks, and support sales teams and partners.
What they ask for
- BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or another Engineering field, or equivalent experience.
- 4+ years of work-related experience in the high-tech electronics industry, particularly in system design or technical customer support roles.
- Technical ability to analyze logs and deployment data and guide customers toward resolving technical issues.
- Excellent written and oral communication skills in English.
Nice to have
- Experience in a Hyperscaler or Cloud Service Provider (CSP) environment, or extensive strategic-partner engagement with them.
- Hands-on NVIDIA hardware and software experience, including performance tuning and error diagnostics; practical knowledge of NCCL, DCGM, or UFM.
Pay
152,000 USD - 241,500 USD (base salary, Level 3); 184,000 USD - 287,500 USD (base salary, Level 4)
Tools
NVIDIA GPU supercomputers, ARM CPU, H100, GB200, NCCL, DCGM, UFM, Embedded Linux Systems
AI focus
Architects and scales distributed AI infrastructure and supports deep learning inference deployments.
Model-written summary · the original posting is authoritative