Job description

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 Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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 . Applications for this job will be accepted at least until September 18, 2026. 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.

Requirements

Must-haves

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!
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
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
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 18, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdllmpython

Platform: workday
Effort: long