Software Engineer, SystemML - AI Networking

In this role, you will be a member of the AI Networking 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 software 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 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.

Software Engineer, SystemML - AI Networking 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

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

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)

Preferred Qualifications:

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 Tensor Flow 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 LLMExperience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience in HPC and parallel computing

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

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…