Software Engineer, SystemML - AI Networking

At the high level, the team aims to enable Meta-wide ML products and innovations to leverage our large-scale GPU training and inference fleet through an observable, reliable and high-performance distributed AI/GPU communication stack. Currently, one of the team’s focus is on building customized features, software benchmarks, performance tuners and software stacks around NCCL and PyTorch to improve the full-stack distributed ML reliability and performance (e.g. Large-Scale GenAI/LLM training) from the trainer down to the inter-GPU and network communication layer. And we are seeking engineers to work on the space of GenAI/LLM scaling reliability and performance.

Responsibilities

Providing technical leadership for the collective communication library development on Meta's large-scale GPU training infra with a focus on GenAI/LLM scaling

Qualifications

Proven C/C++ and Python programming skills Proven track record of leading successful projects Experience leading cross-functional technical projects and communicating technical decisions to both technical and non-technical stakeholders Specialized experience in one or more of the following machine learning/deep learning domains: Distributed ML Training, GPU architecture, ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine Learning frameworks (e.g. PyTorch) Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta Experience with NCCL and distributed GPU performance analysis on RoCE/Infiniband Knowledge of GPU architectures and CUDA programming Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow Experience in AI framework and trainer development on accelerating large-scale distributed deep learning models Experience with both data parallel and model parallel training, such as Distributed Data Parallel, Fully Sharded Data Parallel (FSDP), Tensor Parallel, and Pipeline Parallel Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) PhD in Computer Science, Computer Engineering, or relevant technical field Knowledge of ML, deep learning and LLM Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience in HPC and parallel computing