Software Engineer, SystemML, AI Networking

About the Role

The AI Networking Software team, part of the DC networking organization, develops and owns the software stack around NCCL (NVIDIA Collective Communications Library). This stack enables multi-GPU and multi-node data communication through HPC-style collectives and is critical for multi-GPU distributed training, impacting nearly every distributed GPU-based ML workload in Meta Production. The team's objective is to enable Meta-wide ML products and innovations by leveraging the large-scale GPU training and inference fleet through an observable, reliable, and high-performance distributed AI/GPU communication stack. A current focus is on building customized features, software benchmarks, performance tuners, and software stacks around NCCL and PyTorch to enhance full-stack distributed ML reliability and performance, particularly for Large-Scale GenAI/LLM training.

Key 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.

Requirements & Tech Stack

• Proven C/C++ and Python programming skills.
• Specialized experience in one or more of the following: Distributed ML Training, GPU architecture, ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine Learning frameworks (e.g. PyTorch).

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Proven track record of leading successful projects.
• Experience leading cross-functional technical projects and communicating technical decisions to both technical and non-technical stakeholders.
• 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.