Summary:

　　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 NCCL (NVIDIA Collective Communications Library), which enables multi-GPU and multi-node data communication through HPC-style collectives. NCCL has been integrated into PyTorch and is on the critical path of multi-GPU distributed training. In other words, nearly every distributed GPU-based ML workload in Meta Production goes through the SW stack the team owns.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 SW benchmarks, performance tuners and SW stacks around NCCL and PyTorch to improve the full-stack distributed ML performance (e.g. Large-Scale GenAI/LLM training) from the trainer down to the inter-GPU and network communication layer. And we are seeking for engineers to tech-lead the space of GenAI/LLM scaling and performance.

　　Required Skills:

　　Software Engineer, SystemML - Scaling / Performance Responsibilities:

　　Tech-leading the overall distributed ML enablement and performance on Meta's large-scale GPU training infra with a focus on GenAI/LLM scalingMinimum Qualifications:

　　Minimum Qualifications:

　　Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.

　　Proven C/C++ and Python programming skills

　　Proven track record of leading successful projects

　　Effective leadership and communication skills

　　Preferred Qualifications:

　　Preferred Qualifications:

　　PhD in Computer Science, Computer Engineering, or relevant technical field

　　Experience with NCCL and distributed GPU performance analysis 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

　　Public Compensation:

　　$177,008/year to $251,000/year + bonus + equity + benefits

　　Industry: Internet

　　Equal Opportunity:

　　Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

　　Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].