Research Scientist, AI Networking (PhD)

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, SW benchmarks, performance tuners and SW 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. We are seeking engineers to work on the space of GenAI/LLM scaling reliability and performance.

Responsibilities

• Enabling reliable and highly scalable distributed ML training on Meta's large-scale GPU training infra with a focus on GenAI/LLM scaling

Minimum Qualifications

• 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
• Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• Specialized experience in one or more of the following machine learning/deep learning domains: High speed networking (RDMA), Distributed ML Training, GPU architecture, ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine Learning frameworks (e.g. PyTorch)
• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment

Preferred Qualifications

• Knowledge of ML, deep learning and LLM
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences
• Experience in HPC and parallel computing
• Knowledge of GPU architectures and CUDA programming
• Experience with NCCL/RCCL/OneCCL and distributed GPU reliability/performance improvement on RoCE/Infiniband
• Experience with both data parallel and model parallel training, such as Distributed Data Parallel, Fully Sharded Data Parallel (FSDP), Tensor Parallel, and Pipeline Parallel
• Experience working and communicating cross-functionally in a team environment
• Experience in AI framework and trainer development on accelerating large-scale distributed deep learning models
• Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow

$121,992/year to $181,000/year + bonus + equity + benefits