NVIDIA has been transforming accelerated computing with innovation that’s fueled by great technology—and amazing people. As part of Nvidia's applied AI team for chip design, you will have the opportunity to tap into the unlimited potential of AI and change the landscape of the chip industry. Our team operates at the intersection of research, engineering, and product development, transforming innovative ideas and research breakthroughs into real-world solutions.  

You will collaborate closely with researchers to design and scale agents - enabling them to reason, plan, call tools and code just like human engineers. You will work on building and maintaining the core infrastructure for deploying and running these agents in production, powering all our agentic tools and applications and ensuring their seamless and efficient performance. If you're passionate about the latest research and cutting-edge technologies shaping generative AI, this role and team offer an exciting opportunity to be at the forefront of innovation. 

What you'll be doing:  

• Design, develop, and improve scalable infrastructure to support the next generation of AI applications, including copilots and agentic tools. 

• Drive improvements in architecture, performance, and reliability, enabling teams to bring to bear LLMs and advanced agent frameworks at scale. 

• Collaborate across hardware, software, and research teams, mentoring and supporting peers while encouraging best engineering practices and a culture of technical excellence. 

• Stay informed of the latest advancements in AI infrastructure and contribute to continuous innovation across the organization. 

What we need to see: 

• MS or higher degree (or equivalent experience) in Computer Science, Engineering, AI, or a related technical field, with 5+ years of hands-on software engineering experience building production-grade software systems, and demonstrated experience shipping AI/LLM-powered applications, agents, or automation workflows into real production environments.

• Strong Python engineering skills are preferred, with the ability to design, prototype, and productionize AI-enabled services, APIs, integrations, automation workflows, and internal tools.

• Practical experience building LLM-powered agents or agentic workflows, with hands-on use of Claude Code, OpenAI Codex, Cursor, GitHub Copilot, or equivalent coding agents to improve real software development workflows.

• Solid software engineering fundamentals and production mindset, including system design, API design, testing, CI/CD, code quality, observability, security, databases, containers, and distributed or event-driven systems.

• Ability to identify repetitive, high-friction, or knowledge-intensive workflows and turn them into practical AI-enabled tools, automations, or assistants that improve productivity and operational efficiency.

• Demonstrated end-to-end ownership of engineering solutions, from architecture and development to deployment, integration, and ongoing operations/support.

•  Excellent communication skills and a collaborative, proactive approach.

Ways to stand out from the crowd:

• Strong ability to connect AI applications and agents with existing systems, services, databases, documentation, codebases, and enterprise workflows in a secure, reliable, and maintainable way. Experience with emerging integration patterns such as MCP, Skills, or similar frameworks is a plus.