3 days ago
Shanghai, ChinaIntern
Responsibilities
- Design and implement an AI-assisted workflow for analyzing and triaging failures in deep learning compiler CI.
- Use logs, test results, and execution metadata to classify failures, identify regressions, and detect flaky tests.
- Integrate the workflow with existing CI infrastructure and create diagnostics for resolving 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.
Requirements
- Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
- Strong Python programming skills and 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.
- Preferred experience includes Jenkins, GitLab CI, GitHub Actions, Buildkite, AI- or LLM-assisted developer tools, Docker, Kubernetes, Slurm, cloud platforms, observability tools, GPU computing, CUDA, machine learning frameworks, LLVM, MLIR, XLA/HLO, or Triton.
Tech Stack
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.