NVIDIA is seeking a Software Engineering Intern to improve the continuous integration systems used to develop and verify our deep learning compilers software. During the internship, you will own a focused project that makes CI failures faster to detect, understand, and resolve.
Working with a mentor and engineers across compiler verification and infrastructure, you will build an AI-assisted workflow for analyzing test results, classifying failures, identifying regressions, and reducing manual triage. You will evaluate your solution on real compiler workloads and integrate successful improvements into the team's CI infrastructure.
What you'll be doing
- Design and implement an AI-assisted workflow for analyzing and triaging failures in deep learning compilers CI
- Use logs, test results, and execution metadata to classify failures, identify regressions, and detect flaky tests
- Integrate the solution with existing CI infrastructure and create diagnostics that help engineers understand and resolve failures
- Evaluate accuracy, reliability, and developer time savings across representative GPU hardware and software configurations
- Collaborate with compiler verification and infrastructure engineers, document the project, and present results to the team
What we need to see
- Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field
- Strong Python programming skills and a solid understanding of software engineering, debugging, and version control
- Familiarity with Linux development environments and command-line tools
- Experience through coursework, research, open-source work, or prior internships with CI/CD, test automation, developer tools, infrastructure, or machine learning systems
- Strong analytical, communication, and collaboration skills, with enthusiasm for improving engineering quality and developer productivity
Ways to stand out from the crowd
- Experience with CI platforms such as Jenkins (groovy), GitLab CI, GitHub Actions, Buildkite, or similar systems
- Experience building AI- or LLM-assisted tools for log analysis, test generation, failure triage, or developer support
- Familiarity with Docker, Kubernetes, Slurm, cloud platforms, or observability tools
- Exposure to GPU computing, CUDA, machine learning frameworks, or compiler technologies such as LLVM, MLIR, XLA/HLO, or Triton
NVIDIA is widely considered one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us. If you are creative, curious, and passionate about building reliable developer infrastructure and applying AI to real engineering problems, we want to hear from you.