Software Engineering Manager, PyTorch MTIA Frameworks - Meta

The MTIA (Meta Training & Inference Accelerator) Software team is part of AI Infra PyTorch org. The team’s mission is to explore, develop and help productize high-performance software and hardware technologies for AI at datacenter scale. Team has been developing AI frameworks to accelerate Meta’s DL/ML workloads on the specialized MTIA AI accelerator hardware. As part of the AI acceleration software stack, we develop Pytorch compiler frontend for MTIA, Pytorch runtime for inference & training, high performance kernel libraries exploiting various hardware architectural features and tooling.We are looking for an engineering manager to support MTIA software stack development for inference and training platform.

RESPONSIBILITIES

Software Engineering Manager, PyTorch MTIA Frameworks Responsibilities:

• Manage team of domain experts developing AI acceleration software stack for MTIA
• Operate strategically and tactically. Develop the vision and strategy to help set direction for the team, while managing day-to-day software development.
• Communicate and collaborate effectively with peer engineering teams.
• Manage a diverse team of developers, help them develop their careers, assigning them to projects tailored to their skill levels, long-term skill development, personalities, and work styles
• Work closely with dedicated recruiting staff to expand the team, including sourcing candidates, interviewing candidates, participating in conferences/events, and on-boarding new employees

MINIMUM QUALIFICATIONS

Minimum Qualifications:

• At least 2 years of experience in managing a software team in a fast-paced capacity.
• Demonstrated experience recruiting, building, structuring, leading technical organizations, including performance management.
• Experience developing AI inference and/or training software stack.

PREFERRED QUALIFICATIONS

Preferred Qualifications:

• Experience working with deep learning frameworks such as PyTorch, TensorFlow, JAX, ONNX, MXNet, TensorRT etc
• Knowledge of deep learning models such as recommendation, ranking, LLM etc.
• Familiarity with AI hardware such as GPUs, deep learning accelerators is a plus