Software Engineering Manager, 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. The program carries top-level executive visibility as a key AI infrastructure initiative, and the team values deep technical contributions with extensive knowledge-sharing., 1. You will drive the 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. You will stay technically engaged, contributing hands-on through AI tools to evaluate the technical direction and quality across your teams. You will form trusted cross-functional partnerships with hardware, frameworks, and model teams while actively shaping strategy and roadmap. You will build and mentor engineers into AI-era leaders while maintaining engineering quality through code reviews, testing practices, and documentation

1. At least 5 years of experience in managing a software team

2. Demonstrated experience in managing other managers

3. Proven understanding and experience in executing full product life-cycles (prototyping, deployment, and support)

Preferred Qualifications:

Preferred Qualifications:

1. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

2. Knowledge of ML frameworks like PyTorch, TensorFlow, ONNX, MXNet, etc

3. Cycle-accurate and functional simulators. RISC-V architecture and custom ISA tooling

4. Triton and domain-specific languages. Experience working closely with hardware architectures such as Intel SIMD, GPU, RISC-V, AI accelerators etc

5. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

6. Experience with different programming models for high-performance computations, e.g., GPU CUDA programming or OpenCL or OpenMP programming

7. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

8. Experience in hardware-software development environments such as simulators, FPGA emulators, etc

9. Experience in accelerating libraries on AI hardware, similar to cuBLAS, cuDNN, CUTLASS, HIP, ROCm etc