Senior Software Engineer, AI Infrastructure Solutions

NVIDIA•Santa Clara, CA

1d•$152,000 - $241,500

About The Position

NVIDIA’s Modular Diagnostics team develops diagnostic applications and infrastructure to test NVIDIA GPUs and ensure performance before launch. The Senior Software Engineer, AI Infrastructure Solutions will build AI-powered automation that accelerates engineering workflows and improves GPU validation speed and quality. The role focuses on developing agentic AI systems, scalable AI infrastructure, and end-to-end intelligent workflows for engineering teams. It also partners across Modular Diagnostics and adjacent organizations to identify use cases, define requirements, prototype solutions, and deploy production-ready systems. The engineer will establish evaluation, observability, security, and reliability practices, and drive projects from concept through adoption while measuring and improving their impact.

Requirements

BS or MS degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
5+ years delivering production software, infrastructure, platform, or AI solutions.
Strong programming skills in Python and C/C++.
Track record of building applications with large language models, AI agents, retrieval-augmented generation, tool use, or workflow orchestration.
Demonstrated ability to build scalable services, APIs, data pipelines, and production software infrastructure.
Strong problem-solving, debugging, and system-design skills, with a history of shipping reliable systems.
Ability to collaborate across technical and business teams, communicate clearly, and drive solutions from idea to production.

Nice To Haves

Background in GPU computing, CUDA, GPU architecture, or silicon bring-up processes.
Familiarity with engineering diagnostics, hardware development workflows, or systems software.
Hands-on work with MLOps, model evaluation frameworks, AI observability tools, or production AI monitoring.
Contributions to open-source AI/ML projects or developer tools.
Prior work building internal developer platforms or engineering efficiency tooling.

Responsibilities

Build and deliver agentic AI solutions to automate silicon bring-up, testing and validation frameworks, debugging, and engineering workflows.
Build scalable AI infrastructure, services, APIs, and reusable platforms for teams developing and deploying AI-powered applications.
Integrate AI agents with engineering tools, knowledge bases, data platforms, and existing business processes.
Partner with multifunctional teams to identify high-impact use cases, define requirements, develop prototypes, and deploy production-ready solutions.
Establish evaluation, observability, security, and reliability practices for AI systems.
Drive projects from concept through adoption, measure outcomes against success criteria, and continuously improve efficiency.