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Software Engineer, SystemML - AI Networking

Meta

United StatesJob$184–257K/yrSeen 1 day agoSeen in employer's feed 1 day ago

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At a glance

Compensation
$184–257K/yr
Location
United States
Work Authorization
Not specified

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Job overview

The role is on Meta's AI Networking Software team, developing the software stack around NCCL for multi‑GPU and multi‑node communication. The team focuses on building features, benchmarks, and performance tuners to improve large‑scale GenAI/LLM training reliability and performance across Meta's GPU fleet.

Skills & qualifications

RequiredNice to have

Skills

C/C++PythonLeadershipCommunicationHPCGPU ArchitectureML SystemsDistributed ML TrainingAI InfrastructurePerformance OptimizationsMachine Learning FrameworksAI Tools IntegrationResponsible AI PracticesPrompt EngineeringAgent OrchestrationNCCLRCCLOneCCLRDMARoCE/InfinibandCUDA ProgrammingGPU ArchitecturesDeep LearningLLMData Parallel TrainingModel Parallel TrainingDistributed Data ParallelFully Sharded Data ParallelTensor ParallelPipeline ParallelCaffe2TensorFlowAI Framework DevelopmentTrainer Development

Qualifications

PhD in Computer Science, Computer Engineering, or Relevant Technical FieldProven Track Record of Leading Successful Projects

Full job description

Summary:

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 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 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. And we are seeking for engineers to work on the space of GenAI/LLM scaling reliability and performance.

Required Skills:

Software Engineer, SystemML - AI Networking Responsibilities:

  1. Tech-leading the collective communication library development on Meta's large-scale GPU training infra with a focus on GenAI/LLM scaling

Minimum Qualifications:

Minimum Qualifications:

  1. Proven C/C++ and Python programming skills

  2. Proven track record of leading successful projects

  3. Effective leadership and communication skills

  4. Specialized experience in one or more of the following machine learning/deep learning domains: HPC, GPU architecture, ML systems, Distributed ML Training, AI infrastructure, performance optimizations, or Machine Learning frameworks (e.g. PyTorch)

Preferred Qualifications:

Preferred Qualifications:

  1. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

  2. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

  3. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

  4. Experience with HPC collective communication and parallel computing libraries such as NCCL, RCCL, OneCCL, RDMA transports, distributed GPU performance analysis on RoCE/Infiniband

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

  6. Knowledge of GPU architectures and CUDA programming

  7. Knowledge of ML, deep learning and LLM

  8. Experience with both data parallel and model parallel training, such as Distributed Data Parallel, Fully Sharded Data Parallel (FSDP), Tensor Parallel, and Pipeline Parallel

  9. Experience working with DL frameworks like PyTorch, Caffe2 or TensorFlow

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

Public Compensation:

$183,997/year to $257,000/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].

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