Software Engineering Manager, ML/AI Frameworks - Meta

The MTIA (Meta Training & Inference Accelerator) Software team has been developing AI frameworks to accelerate Meta’s DL/ML workloads on the specialized MTIA AI accelerator hardware in a highly performant and flexible way. As part of the AI acceleration software stack, we develop kernel libraries exploiting various hardware architectural features, achieving high performance for our inference and training workloads.We are looking for a Software Engineering Manager to support a team of kernel engineers and drive high-performance DL kernel library development and performance tuning specific to the MTIA AI accelerator hardware.

RESPONSIBILITIES

Software Engineering Manager, ML/AI Frameworks Responsibilities:

• Grow a team of domain experts in high performance DL kernel development.
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Operate strategically and tactically. Develop vision, strategy and help set direction for the team.
• Remain up-to-date on ongoing software development activities in the team, help work through technical challenges, and be involved in design decisions.

MINIMUM QUALIFICATIONS

Minimum Qualifications:

• Experience with deep learning kernel development on CPU, GPU or AI accelerators
• 2+ years of experience in managing a team of kernel engineers of varied skill levels.
• Experience with cross functional collaboration with hardware or AI framework/compiler teams.
• Demonstrated experience recruiting, building, structuring, leading technical organizations, including performance management.

PREFERRED QUALIFICATIONS

Preferred Qualifications:

• Experience in accelerating libraries on AI hardware, similar to cuBLAS, cuDNN, CUTLASS, HIP, ROCm etc
• Experience with different programming models for high-performance computations, e.g. GPU CUDA programming or OpenCL or OpenMP programming.
• Experience working closely with hardware architectures such as Intel SIMD, GPU, RISC-V, ML Accelerators etc.
• Experience in hardware-software development environments such as simulators, FPGA emulators etc
• Knowledge of ML frameworks like PyTorch, TensorFlow, ONNX, MXNet, etc.