AI Research Manager

Meta Meta · Big Tech · Menlo Park, CA

AI Research Manager at Meta to lead teams working on large-scale ML systems, foundation models, and applied AI innovations, driving strategy and execution with a focus on research-to-product impact and team development.

What you'd actually do

  1. Lead and manage multiple teams of AI researchers and technical leaders delivering large-scale machine learning and AI research projects with significant product impact
  2. Shape and actively influence the AI research strategy and roadmap, translating long-term research goals into prioritized, executable plans
  3. Drive adoption of AI tools and workflows across the team to expand delivery capacity and elevate engineering and research craft
  4. Maintain hands-on technical engagement by evaluating model architectures, research directions, and technical quality using AI-assisted tools and direct contribution
  5. Partner with product, design, and data science teams to ensure AI research outputs translate into measurable product improvements and user value

Skills

Required

  • 8+ years of experience in AI, machine learning, or a closely related research or engineering field
  • 4+ years of experience managing teams of researchers or engineers, including experience managing other people managers or technical leaders
  • Experience defining and executing AI or machine learning research strategy across multiple concurrent projects with cross-functional dependencies
  • Track record of delivering AI or machine learning systems from research through production at scale, with demonstrated product or business impact
  • Experience communicating technical AI research direction and outcomes to both technical and non-technical stakeholders through written documents and presentations
  • Demonstrated ability to integrate AI-native tooling and workflows into team processes to accelerate research velocity and quality
  • Experience leading AI research teams working on foundation models, large language models, generative AI, or multimodal learning systems
  • Track record of publishing or shipping novel AI research with measurable impact on product systems or the broader research community
  • Experience building and scaling AI research organizations, including recruiting and developing researchers across a range of experience levels

Nice to have

  • AI-assisted tools
  • responsible and ethical use of AI
  • model evaluation rigor
  • reproducibility standards

What the JD emphasized

  • delivering large-scale machine learning and AI research projects with significant product impact
  • Track record of delivering AI or machine learning systems from research through production at scale, with demonstrated product or business impact
  • Track record of publishing or shipping novel AI research with measurable impact on product systems or the broader research community

Other signals

  • leading AI research teams
  • foundation models
  • large-scale machine learning systems
  • product impact