Join NVIDIA's GPU SW Kernel organization, where we're groundbreaking the next generation of outstanding AI systems to transform GPU software development. Our team leads the effort in crafting agentic AI systems that improve the quality and efficiency of GPU software. This is an outstanding opportunity to grow innovative systems from promising projects to critically important infrastructure. Be part of a dynamic team that impacts GPU release quality and establishes new standards in AI-enhanced automation!
What you'll be doing:
Compose, build, and productize agentic AI systems that operate directly on GPU software artifacts — source code, crash dumps, changelists, test results, and CI/CD signals
Own the full lifecycle of agentic services: from prototype through evaluation, deployment, and continuous improvement
Build robust LLM-powered pipelines for code generation, automated bug triage, regression detection, and intelligent test coverage analysis
Develop the agentic knowledge layer and MCP-based infrastructure that allow agents to interact with internal developer systems (Gerrit, NVBugs, CI/CD, Perforce, Slack)
Design and assess agent quality thoroughly — establish metrics, develop evaluations, measure precision/recall tradeoffs at each stage of the funnel
Work closely with GPU SW kernel engineers to pinpoint high-impact automation opportunities and convert domain knowledge into effective agentic AI systems
Drive adoption across GPU SW teams by shipping agentic services that are fast, reliable, and meaningfully better than the manual alternative!
What we need to see:
BS/MS in Computer Science, Computer Engineering, or equivalent experience
8+ years of software engineering experience, including a minimum of 2 years developing production LLM or agentic AI systems
Strong Python skills and experience with modern LLM frameworks (LangChain, LlamaIndex, or equivalent)
Hands-on experience with RAG pipelines, tool-use / function-calling patterns, multi-step agentic services, and timely engineering at scale
Proven experience delivering and refining agentic AI systems within a live production setting
Solid software engineering fundamentals: system build, testing, observability, and scalable architecture
Strong analytical skills — comfortable reasoning about precision/recall tradeoffs, false positive rates, and evaluation methods for agent outputs
Ways to stand out from the crowd:
Experience building code agents — automated generation, review, or repair of real codebases
Background in GPU, driver, or systems software (even as a user or collaborator) and familiarity with CUDA, kernel-mode software, or GPU driver development workflows
Experience with MCP servers, tool-calling agents, or multi-agent orchestration frameworks
Contributions to open-source AI tooling, agentic frameworks, or developer efficiency tools
Familiarity with CI/CD systems, crash analysis pipelines, or automated regression triage
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
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
- BS or MS in Computer Science, Computer Engineering, or equivalent experience
- 8+ years of software engineering experience
- At least 2 years developing production LLM or agentic AI systems
- Strong Python skills
- Experience with modern LLM frameworks such as LangChain, LlamaIndex, or equivalent
- Hands-on experience with RAG pipelines, tool-use or function-calling patterns, and multi-step agentic services
- Experience delivering and refining agentic AI systems in production
- Strong software engineering fundamentals, including system building, testing, observability, and scalable architecture
- Strong analytical skills involving precision/recall tradeoffs, false-positive rates, and agent-output evaluation
- Experience building code agents for automated code generation, review, or repair
- Background in GPU, driver, or systems software, including familiarity with CUDA, kernel-mode software, or GPU driver workflows
- Experience with MCP servers, tool-calling agents, or multi-agent orchestration frameworks
- Contributions to open-source AI tooling, agentic frameworks, or developer-efficiency tools
- Familiarity with CI/CD systems, crash analysis pipelines, or automated regression triage
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 — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
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.”
