In this role, you will be a member of the Network AI Software team and part of the bigger DC networking organization. The team develops and owns the software stack around collective communication libraries around Meta. At the high level, the team aims to enable Meta-wide ML products and innovations to leverage our large-scale training and inference fleet through an observable, reliable and high-performance distributed AI communication stack. Currently, one of the team's focus is on building customized features, SW benchmarks, performance tuners and SW stacks around PyTorch to improve the full-stack distributed ML reliability and performance (e.g. Large-Scale GenAI/LLM training) from the trainer down to the network communication layer. And we are seeking for leaders to work on the space of GenAI/LLM scaling reliability and performance.

Software Engineering Manager, AI Networking Responsibilities

• Help define technical roadmap for the team, drive execution of associated tasks and support the team in resolving dependencies


• Collaborate effectively with other groups such as Hardware, Infrastructure, Operations


• Interact with external partners as needed in resolving dependencies associated with objectives


• Guide and help team members develop appropriate skillsets to grow in their careers, and where necessary address under performance


• Communicate cross-functionally and drive engineering efforts

Minimum Qualifications

• BS or MS in Computer Science or related technical discipline or equivalent experience


• 2+ years experience managing a networking related Software Engineering Team


• Working knowledge of network transport stack such as RoCE (RDMA)


• Experience with software development for Distributed and Embedded systems


• Experience recruiting and managing Software Engineers

Preferred Qualifications

• Experience with NCCL and distributed GPU reliability/performance improvment on RoCE/Infiniband


• Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow


• Knowledge of ML, deep learning and LLM

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