Meta is seeking a principal-level Compiler Architect to drive the technical strategy and execution of compiler infrastructure for MTIA (Meta Training and Inference Accelerator). In this role, you will define the compiler architecture that enables efficient code generation, optimization, and execution on Meta's custom AI accelerators. 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 shape the foundational software stack that unlocks the full performance potential of custom silicon for large-scale AI workloads.

Responsibilities

Define and own the compiler architecture and technical roadmap for MTIA, including graph compilers, code generation, and optimization strategies
• Solve complex compiler optimization challenges spanning operator fusion, memory planning, scheduling, and efficient mapping of ML workloads to custom accelerator hardware
• Design extensible compiler frameworks and intermediate representations that enable rapid iteration and support evolving ML model architectures
• Drive performance improvements by identifying and eliminating bottlenecks across the compiler stack, from high-level graph optimizations to low-level code generation
• Partner with MTIA hardware teams on hardware-software co-design, influencing accelerator architecture decisions based on compiler capabilities and workload requirements
• Establish compiler correctness, reliability, and performance validation practices that ensure production-quality code generation at scale
• Collaborate with ML framework teams to ensure seamless integration of MTIA compiler infrastructure with PyTorch and other ML frameworks
• Evaluate and integrate state-of-the-art compiler technologies such as MLIR, and drive adoption of best practices across the compiler organization
• Mentor engineers across the organization, leading compiler architecture reviews and establishing a culture of technical excellence in compiler development
• Communicate complex compiler architecture and strategy clearly to technical and non-technical audiences, producing reference-quality design documents

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of 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 in C++ 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

Preferred Qualifications
• Experience with hardware-software co-design for custom ML accelerators or AI chips
• Track record of applying AI tools and automation to redesign engineering workflows, with demonstrated efficiency or quality improvements
• Master's or PhD degree in Computer Science, Computer Engineering, or a related technical field
• Experience with graph-level optimizations, operator fusion, memory planning, and scheduling for ML workloads
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 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
• Deep understanding of ML model architectures (transformers, CNNs, etc.) and their computational patterns
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with ML compiler stacks such as MLIR, XLA, TVM, Glow, or similar frameworks