
Senior System Software Engineer, Agentic Kernel Development
Required skills
- agentic AI systems
- GPU software artifacts
- source code
- crash dumps
- changelists
- test results
- CI/CD signals
- LLM-powered pipelines
- code generation
- automated bug triage
Nice to have
- code agents
- automated code generation
- automated code review
- automated code repair
- GPU
- driver
Role summary
NVIDIA is seeking a Senior System Software Engineer to join their GPU SW Kernel organization, focusing on developing agentic AI systems for GPU software. The role involves the full lifecycle of AI services, from prototyping to production, including building LLM-powered pipelines for code generation, bug triage, and test analysis. Responsibilities include developing the agentic knowledge layer, designing and assessing agent quality, and collaborating with engineers to identify automation opportunities. The ideal candidate will have 8+ years of software engineering experience, including 2 years in production LLM or agentic AI systems, strong Python skills, and experience with modern LLM frameworks and RAG pipelines. Familiarity with GPU software, CI/CD, and system design is also valued.
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
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.Sample NVIDIA interview questions
- 1
Design a system for a rock paper scissors game
system designmedium - 2
Implement a distributed data migration management platform.
system designmedium - 3
Design a Distributed Build System with Cross-Data Center Artifact Sharing
system designhard - 4
Design a distributed training system for a trillion-parameter language model
system designmedium - 5
Develop a distributed tracing system for tracking and debugging.
system designmedium
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