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Meta · 2 days ago

Research Scientist, AI Networking (PhD)

Menlo Park, CA

Full-time

Onsite

Senior Level

Meta builds technologies that help people connect, find communities, and grow businesses. In this role, you will be a member of the AI Networking Software team, focusing on enabling reliable and highly scalable distributed ML training on Meta's large-scale GPU training infrastructure, with an emphasis on GenAI/LLM scaling.

Computer Software

Comp. & Benefits

Responsibilities

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

Qualification

PhD in Computer ScienceMachine Learning frameworksHigh speed networkingDistributed ML TrainingGPU architectureNCCL/RCCL/OneCCLCUDA programmingHPCParallel computingPyTorchSoft skills

Required

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

Experience with NCCL/RCCL/OneCCL and distributed GPU reliability/performance improvement on RoCE/Infiniband

Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow

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 in AI framework and trainer development on accelerating large-scale distributed deep learning models

Experience in HPC and parallel computing

Knowledge of GPU architectures and CUDA programming

Knowledge of ML, deep learning and LLM

Experience working and communicating cross-functionally in a team environment

Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences

Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)