Senior Compiler Engineer Infrastructure

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +5 · Remote

Senior Compiler Engineer Infrastructure role focused on aligning NVIDIA's compiler codebases with open-source ecosystems and improving developer productivity at scale. This role involves reconciling downstream compiler repositories with upstream open-source projects like LLVM, Clang, and MLIR, and building tooling and infrastructure for compiler engineers.

What you'd actually do

  1. Reconcile and synchronize downstream compiler codebases with open-source repositories, including restructuring, refactoring, and upstreaming internal changes where appropriate
  2. Lead efforts to restructure, merge, or retire internal code to reduce divergence from upstream open-source projects
  3. Design and build infrastructure, tooling, and developer workflows that improve productivity, correctness, and maintainability for internal compiler engineers
  4. Develop automation and developer tools to aid in rebasing, patch management, validation, and large-scale refactoring
  5. Explore and apply AI-assisted tools to improve developer workflows, including code navigation, change analysis, refactoring assistance, testing, and review efficiency

Skills

Required

  • B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or related field (or equivalent experience)
  • Experience with open-source compiler frameworks
  • Excellent hands-on C++ programming skills
  • 3+ years experience working with large-scale, long-lived codebases, including refactoring and restructuring efforts
  • Solid understanding of compiler internals, including IRs, passes, build systems, and toolchains
  • Familiarity with source-control–heavy workflows (e.g., downstream vs. upstream repos, patch queues, rebasing strategies)
  • Strong software engineering fundamentals with an emphasis on robust, maintainable developer infrastructure
  • Good communication and documentation skills; ability to collaborate across teams and time zones

Nice to have

  • Direct experience reconciling or maintaining downstream forks of open-source projects
  • Experience building developer productivity tools, CI infrastructure, or large-scale automation
  • Practical experience applying AI or ML-based tools to improve engineering workflows
  • Background in GPU programming, CUDA, or parallel programming models
  • Familiarity with deep learning frameworks and performance-sensitive workloads on NVIDIA GPUs

What the JD emphasized

  • AI-assisted tools to improve developer workflows