About The Position
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!
Requirements
- 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
Nice To Haves
- 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
Responsibilities
- 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!
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
