NVIDIA’s Worldwide Field Operations (WWFO) team is looking for a Solution Architect with expertise in Generative AI, Data Science applications and Machine Learning (ML) to work with our Hyperscaler partners. In our Solutions Architecture team, we work with the most exciting platform for accelerated computing and drive the latest breakthroughs in artificial intelligence. We need individuals who can enable customer and partner productivity. Our goal is to develop long-lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone who is always thinking about artificial intelligence, someone who can maintain alignment in a fast paced and constantly evolving field.
You will be working with the latest NVIDIA technologies coupled with the most advanced CSP infrastructures, changing the way people interact with technology. As a Solutions Architect, you will be the first line of technical expertise between NVIDIA, our Hyperscaler partners and our end-customers. For this role, the primary focus will be on our strategic partnership with Google (GCP). Your duties will vary from working on proof-of-concept demonstrations, to driving relationships with key technical executives and managers to evangelize accelerated computing and Generative AI. Dynamically engaging with developers, researchers, data scientists, IT managers and senior leaders is a meaningful part of the Solutions Architect role and will give you experience with a range of challenges and solutions.
What You'll Be Doing:
Develop and demonstrate solutions based on Hyperscalers and NVIDIA’s pioneering GenAI software and hardware technologies to developers
Work directly with key customers and our Hyperscalers partners to understand their challenges and provide the best solutions based on NVIDIA products
Perform in-depth analysis and optimization to ensure the best performance on GPU-accelerated systems using NVIDIA software platform. This includes support in optimization of both training and inference pipelines
Partner with Engineering, Product and Sales teams to understand developer’s challenges and plan for the best suitable solutions. Enable development and growth of product features through customer feedback and proof-of-concept evaluations
Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures
What We Need to See:
5+ years of Solutions Architect/Engineering or similar experience in AI-related fields
Excellent ability to listen, both verbal and written communication skills, and being comfortable with presenting technical solutions in English
Expertise in deploying large-scale training and inferencing pipeline on Hyperscaler’s infrastructure, with a focus on GCP
MS/PhD or equivalent in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields
A proven track record of academic and/or industry experience in fields related to machine learning, deep learning and/or data science
You are excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team)
You are a self-starter with interest in growth, passion for continuous learning and sharing findings across the team
Ways to Stand Out from The Crowd:
Background with Hyperscaler platforms such as GCP
Experience running and optimizing large scale distributed DL training
Experience optimizing inference pipeline, using a range of inferencing technics (e.g., understanding of model compression techniques, model compilation or model serving)
Background with working with larger transformer-based architectures
Experience using DevOps technologies such as Docker, Kubernetes, Singularity, etc.
Skills Required
- 5+ years of Solutions Architect, Solutions Engineering, or similar experience in AI-related fields
- Strong verbal and written communication skills, including the ability to present technical solutions in English
- Experience deploying large-scale training and inference pipelines on hyperscaler infrastructure, especially GCP
- MS, PhD, or equivalent degree in Computer Science, Data Science, Electrical or Computer Engineering, Physics, Mathematics, or another engineering field
- Academic or industry experience in machine learning, deep learning, and/or data science
- Experience collaborating across Engineering, Product, Sales, and Marketing teams
- Background with hyperscaler platforms such as GCP
- Experience running and optimizing large-scale distributed deep learning training
- Experience optimizing inference pipelines using model compression, model compilation, or model serving techniques
- Experience with large transformer-based architectures
- Experience with DevOps technologies such as Docker, Kubernetes, or Singularity
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
