Staff Systems ML Engineer — Scale AI Infrastructure

Meta; Sunnyvale, Menlo Park, Bellevue, Seattle (CA, CA, WA, WA); On-site; USD 260,000 - 360,000; Full time

Job summary

Meta is seeking a Staff Software Engineer to join the Systems ML Engineering team, focusing on building and scaling infrastructure and software systems that power large-scale ML workloads across Meta's production fleet. You will architect and own components of the ML systems stack, spanning training infrastructure, model serving, distributed computing frameworks, and ML platform tooling.

You will work at the intersection of systems engineering and machine learning to drive reliability,

Qualifications

• Bachelor's degree or equivalent practical experience in a relevant field.
• 8+ years of software engineering experience in systems software, distributed computing, or ML infrastructure.
• Experience designing large-scale distributed systems, including training orchestration, model serving, or data pipelines.
• Experience with PyTorch and distributed training.
• Experience with performance profiling and bottleneck identification.
• Experience contributing to or maintaining open-source ML systems or distributed computing projects.

Responsibilities

• Architect and own components of the ML systems stack.
• Scale infrastructure and software systems for large-scale ML workloads across the production fleet.
• Drive reliability, performance, and efficiency for AI workloads including LLMs and generative AI systems.
• Collaborate across teams to deliver end-to-end projects with milestone planning and risk mitigation.

Skills

C++

Python

Distributed systems

ML infrastructure

Performance optimization

Cross-team coordination

Education

Bachelor's degree in CS/CE or related field

Tools

PyTorch

CUDA

Job description

Meta is seeking a Staff Software Engineer to join the Systems ML Engineering team, focusing on building and scaling infrastructure and software systems that power large-scale ML workloads across Meta's production fleet. You will architect and own components of the ML systems stack, spanning training infrastructure, model serving, distributed computing frameworks, and ML platform tooling.

You will work at the intersection of systems engineering and machine learning to drive reliability,