Distributed ML Systems Engineer - Scaling (Equity)

Meta; Menlo Park (CA); On-site; USD 154,000 - 217,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

Meta's Network.AI Software team seeks a Software Engineer, SystemML – Scaling / Performance to enable reliable, scalable distributed ML training on Meta's large-scale GPU training infrastructure, focusing on GenAI/LLM scaling across inter-GPU and network layers.

You will work with NCCL-based stacks, contribute to performance benchmarks and optimizations, and collaborate with PyTorch and ML framework teams to improve full-stack reliability and throughput for distributed AI workloads.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Specialized experience in one or more of: Distributed ML Training, GPU architecture, ML systems, AI infrastructure, high performance computing, performance optimizations, or ML frameworks (e.g. PyTorch).

Responsibilities

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

Skills

Distributed ML Training

GPU architecture

ML systems

AI infrastructure

High performance computing

Performance optimizations

ML frameworks (e.g. PyTorch)

Education

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

Tools

CUDA programming

PyTorch

Caffe2

TensorFlow

Job description

Meta's Network.AI Software team seeks a Software Engineer, SystemML – Scaling / Performance to enable reliable, scalable distributed ML training on Meta's large-scale GPU training infrastructure, focusing on GenAI/LLM scaling across inter-GPU and network layers.

You will work with NCCL-based stacks, contribute to performance benchmarks and optimizations, and collaborate with PyTorch and ML framework teams to improve full-stack reliability and throughput for distributed AI workloads.