Software Engineer, LLVM Compiler

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, Tensor Flow, or MLIR-based ML tool chains
• 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)