Job description

Meta is seeking a principal-level Compiler Architect to work across the full compiler stack for MTIA (Meta Training and Inference Accelerator). In this role, you will design and implement compiler infrastructure that enables efficient code generation, optimization, and execution on Meta's custom AI accelerators — from high-level graph transformations through intermediate representations to low-level code generation. You will tackle the hardest compiler challenges spanning ML workload analysis, graph-level optimizations, memory hierarchy management, and hardware-software co-design. This is a role for engineers who build the foundational software stack that unlocks the full performance potential of custom silicon for large-scale AI workloads. Design compiler architecture and contribute across the full MTIA compiler stack, including graph compilers, intermediate representations, optimization passes, and code generation Build extensible compiler frameworks and intermediate representations that enable rapid iteration and support evolving ML model architectures Identify and eliminate performance bottlenecks across the compiler stack — from high-level graph optimizations through scheduling to low-level code generation Collaborate with MTIA hardware teams on hardware-software co-design, translating compiler analysis and workload requirements into accelerator architecture improvements Develop compiler correctness, reliability, and performance validation practices that ensure production-quality code generation at scale Work with ML framework teams to integrate MTIA compiler infrastructure with PyTorch and other ML frameworks Evaluate and apply state-of-the-art compiler technologies such as MLIR, bringing best practices into the compiler organization Mentor engineers across the organization, leading compiler architecture reviews and raising the bar for technical excellence in compiler development Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Experience in software engineering with deep specialization in compiler development, code generation, or performance optimization for accelerators Experience architecting production compiler infrastructure for ML accelerators, GPUs, or custom silicon Experience with compiler intermediate representations, optimization passes, and code generation techniques Experience leading multi-year cross-functional technical initiatives, including defining metrics, managing dependencies, and driving execution across organizational boundaries Experience developing high-performance systems software with strong understanding of low-level optimization and hardware architecture Experience influencing technical direction and engineering practices across multiple teams through written proposals, design reviews, and stakeholder alignment Deep understanding of ML model architectures and their computational patterns Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience contributing to compiler or ML systems efforts through publications, open-source projects, or standards bodies Experience defining and operationalizing performance benchmarks and correctness validation for compiler infrastructure Experience with hardware-software co-design for custom ML accelerators or AI chips Experience with ML compiler stacks such as MLIR, XLA, TVM, Glow, or similar frameworks Track record of applying AI tools and automation to redesign engineering workflows, with demonstrated efficiency or quality improvements Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Master's or PhD degree in Computer Science, Computer Engineering, or a related technical field

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Keywords to mirror

aiml

Platform: meta
Effort: medium