Senior Solutions Architect - Data Center Infrastructure

About the role

This role is responsible for architecting and scaling high-performance, distributed AI infrastructure for NVIDIA's groundbreaking data center products. The ideal candidate will engage deeply with customers, providing technical support and guiding them through the deployment process.

Responsibilities

• Help architect and scale high-performance, distributed AI infrastructure on-prem or in the cloud built with the latest NVIDIA GPU supercomputers for new and existing customers.
• Act as a technical specialist on GPU and networking products, directly supporting sales account managers to secure design wins.
• Provide technical and onsite support to solve complex hardware and software problems, with a focus on deep learning inference.
• Collect, maintain, and analyze complex deployment data and logs to assess product health, identify technical challenges, and guide the customer to resolution of roadblocks.
• Establish and cultivate technical relationships with engineers and architects at key customer accounts.
• Identify customer architectures and key product requirements in the CSP/OEM AI market to efficiently implement NVIDIA's solutions.
• Develop technical solutions including hardware & software demos and example system designs.
• Offer technical and sales training to direct sales teams and channel partners.

Requirements

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields 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 competence to roll up your sleeves, look at logs and deployment data, and guide a customer to the resolution of technical issues.
• Experienced in analytical and problem-solving abilities.
• Capable of excelling in a dynamic, constantly evolving environment.
• Excellent written and oral communication skills in English, with the ability to collaborate effectively with management and engineering teams.

Qualifications

• Experience working in a Hyperscaler or Cloud Service Provider (CSP) environment or extensive experience engaging with them as a strategic partner.
• Hands-on experience with NVIDIA hardware (e.g., ARM CPU, H100, GB200) and software libraries, with an understanding of performance tuning and error diagnostics.
• Established track record of driving a product from the pilot phase to high-volume, at-scale deployment in a data center environment.
• Practical knowledge of NVIDIA systems technology such as NCCL, DCGM, and UFM.
• Knowledge of Embedded Linux Systems, APIs, and similar embedded OS.

Skills

• Strong passion for system design and a successful history of engaging with customers on a deep technical level.
• Direct datacenter knowledge and experience debugging datacenter platforms.
• Ability to serve as a critical bridge between product strategy and large-scale customer deployment.

Benefits

NVIDIA offers competitive compensation, including a base salary range of $152,000 - $241,500 for Level 3 and $184,000 - $287,500 for Level 4, along with equity and benefits.

Pay

The base salary for this position is determined based on location, experience, and the pay of employees in similar positions.

Schedule

The schedule for this role is not specified, but it is expected to be flexible to accommodate the needs of the customer and the company.

Benefits

NVIDIA provides a comprehensive benefits package that includes health insurance, retirement plans, and paid time off.

Application Instructions

Applications for this job will be accepted at least until May 15, 2026.

Ways to Stand Out

• Experience working in a Hyperscaler or Cloud Service Provider (CSP) environment or extensive experience engaging with them as a strategic partner.
• Hands-on experience with NVIDIA hardware (e.g., ARM CPU, H100, GB200) and software libraries, with an understanding of performance tuning and error diagnostics.
• Established track record of driving a product from the pilot phase to high-volume, at-scale deployment in a data center environment.
• Practical knowledge of NVIDIA systems technology such as NCCL, DCGM, and UFM.
• Knowledge of Embedded Linux Systems, APIs, and similar embedded OS.