Software Engineering Manager - AI Hardware Systems Co-Design

MetaSunnyvale, CA
1d$184,000 - $257,000

About The Position

Meta is seeking a hands-on engineering manager to join the Monetization Ranking AI Inference Optimization team. The team works at the intersection of hardware, software and AI technologies, making direct contributions on Ads ranking model, MTIA, Nvidia, AMD and many other cutting edge open-source as well as internal infrastructure projects. The ranking team has established relationships with both academia and industry. We frequently collaborate with academia through internships and have a track record of publications in top AI, systems and architecture conferences. We partner closely with industry leaders to influence their roadmaps and build the best products for Meta’s Infrastructure. Join us and be a part of the team that is shaping the future of Meta Monetization!

Requirements

  • A Master of Science in Computer Science or related fields, with 4+ years of management experience
  • Technical depth in: AI Training and Inference workloads, GPU Systems Architecture, Hardware/Software Codesign
  • Demonstrated experience in leading technical teams working on Artificial Intelligence/Machine Learning systems and related domains
  • Proven track record of managing complex, large-scale programs and navigating the broad, interdisciplinary aspects of such projects
  • Experience in communicating effectively with a broad range of stakeholders, collaborators and clients, at different levels

Nice To Haves

  • A PhD in Computer Science or related fields with a demonstrated track record of research publications in AI/ML systems and related domains
  • Capacity to effectively communicate with and influence executive leadership and decision makers
  • Familiarity with ML architecture design and advanced technology, including quantization, knowledge distillation, foundational modeling

Responsibilities

  • Lead the GPU AI Systems Co-Design Team responsible for driving Meta's Ads model scalability moving forward
  • Analyze trends and insights in Training and Inference workloads across Ads Ranking use-cases to inform strategic decisions
  • Leverage the team's technical expertise to pioneer innovative projects that significantly improve AI performance and efficiency at scale
  • Collaborate cross-functionally across ML, hardware, infrastructure, software and data science teams to drive engineering efforts
  • Build and scale a team of engineers, drive recruitment, career growth, performance management, and strategic goal-setting

Benefits

  • bonus
  • equity
  • benefits
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