Senior Compiler Engineer Infrastructure

NVIDIA•Austin, CA

About The Position

NVIDIA is seeking an experienced Compiler Infrastructure Engineer to join its Compute Compiler Team. The primary focus of this role is to align NVIDIA’s compiler codebases with open-source ecosystems and improve developer productivity at scale. This position is at the intersection of open-source compiler development, internal compiler infrastructure, and developer experience. The engineer will play a key role in reconciling downstream compiler repositories with upstream open-source projects like LLVM, Clang, and MLIR, while also building the necessary tooling, workflows, and infrastructure to accelerate compiler engineers at NVIDIA. The compiler organization's work impacts every NVIDIA GPU. By enhancing the alignment of internal compiler stacks with open source and modernizing developer tools, this role will help NVIDIA maintain its leadership in scalable, maintainable, and community-driven compiler technology. The ideal candidate is passionate about open-source stewardship, large-scale codebase alignment, and leveraging modern tooling, including AI-assisted workflows, to boost developer velocity.

Requirements

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 Haves

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

Responsibilities

Reconcile and synchronize downstream compiler codebases with open-source repositories, including restructuring, refactoring, and upstreaming internal changes where appropriate
Lead efforts to restructure, merge, or retire internal code to reduce divergence from upstream open-source projects
Design and build infrastructure, tooling, and developer workflows that improve productivity, correctness, and maintainability for internal compiler engineers
Develop automation and developer tools to aid in rebasing, patch management, validation, and large-scale refactoring
Explore and apply AI-assisted tools to improve developer workflows, including code navigation, change analysis, refactoring assistance, testing, and review efficiency
Partner with compiler developers, architecture teams, and CI/test infrastructure teams to ensure changes scale across geographically distributed organizations
Serve as a technical bridge between internal compiler development and open-source ecosystems, helping shape long-term alignment strategies