Software Engineering Manager, Compilers, MTIA

As a Software Engineering Manager in the MTIA Software team, you will lead the development of LLVM compiler infrastructure and early software development platforms like simulators, which will be used by the whole software team to accelerate Meta’s AI workloads on custom accelerators. Your work on LLVM compiler infrastructure directly influences ISA design and hardware/software co-design decisions. The simulators you build will be critical for pre-silicon development, allowing the whole team to develop software before hardware is available. This program is a key AI infrastructure initiative, and the the team values deep technical contributions with extensive knowledge sharing., 1. Drive development of the LLVM compiler toolchain and early software development platforms, including cycle-accurate and functional simulators. You will lead teams of engineers and technical leaders delivering large-scale projects across compiler infrastructure and simulator development

1. Stay technically engaged, contributing hands-on through AI tools to evaluate the technical direction and quality across your team
2. Build and mentor engineers into AI-era leaders while maintaining high engineering craft standards
3. Form trusted cross-functional partnerships across software, hardware, and model teams while actively shaping strategy and roadmaps
4. Drive AI adoption across teams to expand delivery capacity, improve engineering workflows, and maintain technical engagement with system architecture and code quality

1. 5+ years of experience managing a software engineering team
2. Experience with compiler architecture and development, particularly ML compilers or domain-specific languages (DSLs)
3. Proven understanding and experience in executing full product life cycles (prototyping, deployment, and support)
4. Experience managing engineering teams, including experience in managing other engineering leaders or technical leads, 1. Experience working closely with hardware architectures such as SIMD, GPU, RISC-V, and AI accelerators
5. Experience with LLVM compiler infrastructure and MLIR
6. Experience with RISC-V architecture and custom ISA tooling
7. Experience in hardware-software development environments such as simulators and emulators
8. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
9. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
10. Knowledge of ML frameworks like PyTorch
11. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
12. Track record of driving AI-native engineering practices within an engineering organization, including integrating AI tools into development and review workflows