Software Engineering Manager, Compilers, MTIA

Meta; Menlo Park, Northern (CA, KY); Hybrid; USD 250,000 - 350,000; Full time

Job summary

Meta is seeking an experienced Software Engineering Manager to lead the MTIA Software team, shaping LLVM compiler infrastructure and simulators for accelerated AI workloads on custom accelerators. You will influence ISA design and hardware/software co-design decisions, driving AI-native practices and knowledge sharing across the organization.

The role emphasizes deep technical contributions, team leadership, and collaboration with hardware teams to advance pre-silicon development and scalable AI

Qualifications

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

Skills

Compiler architecture

LLVM/MLIR

RISC-V tooling

Simulators/Emulators

Hardware-software co-design

AI/ML workflows optimization

Team management

Python

GPUs/AI accelerators

Ethical AI practices

Tools

LLVM compiler infrastructure

MLIR

RISC-V tooling

AI/ML frameworks (PyTorch)

Hardware architectures (SIMD, GPU)

Job description

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.

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