Software Engineer, LLVM Compiler

Meta; Bellevue, Menlo Park, New York (WA, CA, NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Meta in Bellevue, WA seeks a Software Engineer to join the MTIA LLVM Compiler team, building a production compiler stack for Meta's AI accelerators. You will work across compiler, ML, firmware, and ASIC experts to optimize deep learning model compilation and generate efficient binaries for hardware blocks.

The role requires 5+ years in compiler development, proficiency in C++ or Rust, and strong leadership to drive cross-team initiatives.

Qualifications

• Bachelor's degree or equivalent; 5+ years in compiler or code optimization.
• Proficient in C++ or Rust with large-scale software development experience.
• Experience with LLVM/MLIR or similar production compiler codebase.
• Track record delivering major compiler features or optimization passes.
• Experience driving cross-team technical initiatives and roadmap influence.
• Ability to mentor engineers and raise technical standards.
• Experience with hardware architectures (SIMD, GPUs, AI accelerators).
• Familiarity with ML frameworks (PyTorch, TensorFlow).

Responsibilities

• Architect, design, and implement production compiler stack for AI accelerators.
• Collaborate across compiler, ML, firmware, and ASIC teams.
• Optimize code generation and back-end compiler passes for hardware blocks.
• Contribute to upstream LLVM/MLIR projects.
• Mentor engineers and influence roadmap decisions.

Skills

C++

Rust

Compiler development

Performance analysis

Debugging

Code optimization

LLVM/MLIR experience

Cross-team collaboration

Mentoring

AI frameworks experience

Hardware acceleration knowledge

Education

Bachelor's degree in Computer Science/Engineering or equivalent

Tools

LLVM

MLIR

GCC

MSVC

Job description

We are seeking a software engineer to join the MTIA LLVM Compiler team, working on the compiler toolchain for Meta's custom silicon AI accelerators. You will be part of our efforts to architect, design, and implement a production compiler stack targeting next-generation deep learning hardware. The team includes compiler, machine learning, firmware, and ASIC experts, and the work spans from compiling PyTorch models through LLVM-based intermediate representations down to optimized binaries for hardware accelerator blocks.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience5+ 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
• 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)