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Software Engineer, Systems ML

Meta· Machine Learning
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Sunnyvale, CA, Remote$219k - $301kFull-timePosted 3mo ago

About the role

Principal-level software engineer role focusing on defining architectural foundations and roadmaps for large-scale ML infrastructure, spanning training systems, inference pipelines, ML compilers, HPC frameworks, and on-device optimization within Meta’s Systems ML Engineering organization.

Responsibilities

  • Identify and solve the most complex cross-system ML infrastructure challenges spanning training, inference, compiler optimization, and hardware-software co-design, including problems that have resisted prior solution attempts
  • Define extensible architectural standards and technical foundations for ML systems that enable consistency and reliability across multiple engineering organizations
  • Develop and own the multi-year technical roadmap for ML systems infrastructure, balancing short-term delivery with long-term platform health and competitive positioning
  • Leverage AI-native tooling and workflows as a force multiplier to eliminate entire categories of engineering toil and accelerate cross-disciplinary work across the ML systems stack
  • Drive performance improvements across large-scale ML training and inference systems by identifying bottlenecks that span multiple subsystems, ownership boundaries, and abstraction layers
  • Establish invariants, correctness proofs, and systemic reliability practices that prevent whole classes of failures across ML infrastructure pipelines
  • Partner with research, hardware, and product engineering teams to translate theoretical ML systems advances into production infrastructure that delivers measurable efficiency and capability gains
  • Assess emerging AI and computing technologies, evaluate competitive ML infrastructure trends, and influence organizational strategy to ensure technical competitiveness
  • Mentor engineers across the organization by providing customized coaching, leading engineering programs, and establishing a culture of thoroughness and high craft in ML systems development
  • Communicate complex ML systems architecture and strategy clearly to technical and non-technical audiences, producing reference-quality design documents and roadmap artifacts

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 12+ years of experience in software engineering with deep specialization in one or more ML systems domains including AI infrastructure, ML compilers, high-performance computing, GPU architecture, ML frameworks, or on-device optimization
  • Experience architecting and delivering large-scale ML training or inference infrastructure that has had measurable impact across multiple engineering organizations
  • Experience leading multi-year cross-functional technical initiatives, including defining metrics, managing dependencies, and driving execution across organizational boundaries
  • Experience developing high-performance ML systems infrastructure in C++, Python, or CUDA, including work at the intersection of hardware and software
  • Experience influencing technical direction and engineering practices across multiple teams through written proposals, design reviews, and stakeholder alignment

Skills

Required:ML infrastructure architectureC++, Python, CUDACross-functional program management

Benefits

  • benefits information available in posting
Last updated Oct 23, 2026. Interested? Apply on the company site.

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