NVIDIA
Senior Product Architect, K8s-Based AI Infrastructure
$184K–$356K
US, CA, Santa Clara +1 · Senior · Full-time · Posted Aug 20, 2026
- Location
- US, CA, Santa Clara
US, Remote - Experience
- 8+ years architecting datacenter-scale HPC or AI infrastructure as a Principal Architect, Solutions Architect, Principal Engineer, or equivalent.
- Education
- Bachelor’s degree in computer science or related field (or equivalent experience).
About the job
Architects and validates datacenter-scale AI infrastructure products, from product concepts and prototypes through deployment requirements and customer-facing solutions.
What you'll do
- Define product architectures covering performance, scalability, interoperability, and datacenter deployment; prototype, test, and refine designs.
- Collaborate with internal teams, partners, and customers to integrate networking, compute, and storage and develop POCs and MVPs.
- Document designs and create customer and partner content, including technical specifications, user guides, whitepapers, blogs, and curriculum.
What they ask for
- Strong background in technologies for large-scale infrastructure management, complex systems, networks, and storage.
- Extensive experience with DevOps solutions, including Python, Ansible, Container Runtimes, Kubernetes, and data center deployments.
- Familiarity with AI workloads, including agentic and RAG-based workflows, inference at scale, large-scale training and fine-tuning, and model evaluation.
- Exceptional communication skills for explaining technical details to diverse audiences.
Nice to have
- First-hand experience building large-scale AI infrastructure.
- Advanced certifications or publications in AI, deep learning, or related fields; experience leading high-impact initiatives is also noted.
Pay
184,000 USD - 287,500 USD base salary (Level 4); 224,000 USD - 356,500 USD base salary (Level 5)
Tools
Python, Ansible, Container Runtimes, Kubernetes
AI focus
Proposes AI infrastructure and Agentic AI products and architects infrastructure supporting agentic and RAG-based workflows, inference, large-scale training and fine-tuning, and model evaluation.
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