Solution Architect

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 hyperscaler partners.

As a Solutions Architect, you will be the first line of technical expertise between NVIDIA, our hyperscaler partners, and our end-customers. You will work with the latest NVIDIA technologies coupled with the most advanced CSP infrastructures, changing the way people interact with technology.

Responsibilities:

• 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 hyperscaler 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
• Partner with Engineering, Product, and Sales teams to understand developer's challenges and plan for the best suitable solutions
• Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures

Requirements:

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

Preferred Qualifications:

• 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
• Background with working with larger transformer-based architectures
• Expertise in DevOps technologies such as Docker, Kubernetes, Singularity, etc.