Software Engineer, LLVM Compiler

About the Role

The Software Engineer for the LLVM Compiler at Meta will work on the compiler toolchain for custom silicon AI accelerators, focusing on architecting, designing, and implementing a production compiler stack. This role involves collaborating with a team of experts to optimize code generation and improve the performance of deep learning hardware.

What You'll Do

• Design, implement, and optimize LLVM-based code generation targeting Meta's custom machine learning accelerators.
• Apply compiler techniques (JIT compilation, dynamic code generation, optimization passes) to build high-performance simulation infrastructure for accelerator hardware.
• Develop and enhance compiler passes, optimizations, and transformations within the LLVM/MLIR infrastructure to improve performance, correctness, and compilation speed.
• Contribute to the development of intermediate representations, compiler libraries, and analysis tools in the LLVM/MLIR ecosystem.
• Conduct design and code reviews.
• Evaluate generated code quality, debug, diagnose, and drive resolution of compiler and cross-disciplinary system issues.
• Analyze and improve the efficiency, scalability, and stability of the compiler toolchain.
• Interface with other compiler-focused teams (both internal and open-source LLVM community) to evaluate and incorporate innovations.
• Mentor other engineers on compiler engineering best practices and improving engineering quality across the team.

Who You Are

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• 5+ years of experience developing compilers or code optimization software, with demonstrated technical leadership.
• Proficiency in C++ or Rust with experience in large-scale software development, debugging, testing, and performance analysis.
• Experience working within the LLVM/MLIR compiler infrastructure or a comparable production compiler codebase (e.g., GCC, MSVC).
• Track record of designing and delivering significant compiler features or optimization passes end-to-end.
• Experience driving cross-team technical initiatives and influencing roadmap decisions.
• Experience crossing multi-disciplinary boundaries (hardware, ML frameworks, runtime systems) to drive optimal system-level solutions.
• Experience in AI framework development or accelerating deep learning models on hardware architectures.
• Demonstrated ability to mentor engineers and raise the technical bar of a team.

Bonus Points

• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).
• Experience contributing to the upstream LLVM or MLIR projects.
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Experience working closely with hardware architectures such as SIMD, GPU, RISC-V, and AI accelerators.
• Familiarity with a mainstream ML framework such as PyTorch, TensorFlow, or MLIR-based ML toolchains.
• Experience with hardware-specific optimization for accelerators, GPUs, or DSPs.
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
• Experience working and communicating cross-functionally in a team environment.
• Experience with machine-code generation and back-end compiler optimizations such as instruction selection, register allocation, and instruction scheduling.
• Experience with deep learning model compilation, graph compilers, or ML-specific optimization techniques (e.g., operator fusion, tiling, quantization-aware compilation).

Compensation

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