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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- MS or higher degree (or equivalent experience) in Computer Science, Engineering, AI, or a related technical field
- 5+ years of hands-on software engineering experience building production-grade software systems
- Demonstrated experience shipping AI/LLM-powered applications, agents, or automation workflows into production environments
- Practical experience building LLM-powered agents or agentic workflows and hands-on use of Claude Code, OpenAI Codex, Cursor, GitHub Copilot, or equivalent coding agents
- Solid software engineering fundamentals and production mindset (system design, API design, testing, CI/CD, code quality, observability, security, databases, containers, distributed/event-driven systems)
- Ability to identify repetitive or knowledge-intensive workflows and convert them into AI-enabled tools or automations
- Demonstrated end-to-end ownership from architecture through deployment and operations/support
- Excellent communication skills and a collaborative, proactive approach
- Strong Python engineering skills
- Experience connecting AI applications and agents with existing systems, services, databases, codebases, and enterprise workflows; familiarity with integration patterns (MCP, Skills)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
