Solutions Architect, CSP GTM

NVIDIA’s Worldwide Field Operations (WWFO) team is looking for a Solution Architect with expertise in Physical AI, Agentic 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 Physical AI strategy with our Cloud Service Provider partners. 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 Physical AI solutions based on Hyperscalers and NVIDIA’s pioneering 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:

• MS/PhD or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields

• 5+ years in Solutions Architecture/Engineering in AI-related domains

• Excellent ability to listen, both verbal and written communication skills, and being comfortable with presenting technical solutions in English

• Expertise with Physical AI solutions on Hyperscaler’s infrastructure

• A proven track record of academic and/or industry experience in fields related to machine learning, deep learning and/or data science

• Excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team)

• 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 Physical AI solutions and offerings using Hyperscaler platforms

• Experience running and optimizing large scale distributed DL training, optimizing inference pipeline, using a range of inferencing techniques (e.g., understanding of model compression techniques, model compilation or model serving)

• Background with working with larger transformer-based architectures

• Expertise in DevOps technologies such as Docker, Kubernetes, Singularity, etc.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:

85,800 EUR – 148,720 EUR for Level 3; and 108,075 EUR – 187,330 EUR for Level 4.