Principal Systems ML Engineer — Architecture & Strategy

Meta; Menlo Park (CA); On-site; USD 219,000 - 301,000; Full time

Job summary

Meta is seeking a principal-level Software Engineer to drive technical strategy and execution across our Systems ML Engineering organization. You will define architectural foundations that power large-scale machine learning infrastructure, spanning training systems, inference pipelines, ML compilers, high-performance computing frameworks, and on-device optimization.

You will identify and solve the hardest cross-system ML infrastructure challenges, shape multi-year technical roadmaps, and amplify

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of software engineering experience with deep specialization in ML systems including AI infrastructure, ML compilers, HPC, GPU architecture, ML frameworks, or on-device optimization
• Experience architecting and delivering large-scale ML training or inference infrastructure with measurable impact across multiple teams
• Experience leading multi-year cross-functional technical initiatives with metrics, dependencies, and execution across boundaries
• Experience developing high-performance ML systems in C++, Python, or CUDA, including hardware-software co-design

Responsibilities

• Identify and solve complex cross-system ML infrastructure challenges spanning training, inference, compiler optimization, and hardware-software co-design
• Define architectural standards and foundations for ML systems enabling consistency across engineering orgs
• Develop and own multi-year roadmaps for ML systems infrastructure balancing short-term delivery with long-term health
• Mentor engineers and establish a culture of rigor and craftsmanship in ML systems development
• Communicate complex ML systems architecture and strategy to technical and non-technical audiences

Job description

Meta is seeking a principal-level Software Engineer to drive technical strategy and execution across our Systems ML Engineering organization. You will define architectural foundations that power large-scale machine learning infrastructure, spanning training systems, inference pipelines, ML compilers, high-performance computing frameworks, and on-device optimization.

You will identify and solve the hardest cross-system ML infrastructure challenges, shape multi-year technical roadmaps, and amplify