Product Operations Manager, Meta Superintelligence Labs

Meta Meta · Big Tech · Menlo Park, CA

Product Operations Manager for Meta Superintelligence Labs focusing on AI-powered automation and operational excellence. The role involves building and operating autonomous agents for end-to-end workflows like triage and bug resolution, driving product quality, and implementing process and tooling improvements. Responsibilities include owning product quality programs, designing AI agents for issue management, analyzing data for quality trends, and building an evaluation practice for AI solutions.

What you'd actually do

  1. Become an expert in the health and quality of MSL products, identifying trends, regressions, and user pain points through data analysis and hands-on product usage
  2. Own end-to-end product quality programs for your area, including launches, dogfooding, RCAs, SEVs, and app health regressions automating first and operating with high reliability
  3. Design, build, and operate autonomous AI agents and workflows for your product area: bug triage, prioritization, classification, deduplication, and resolution
  4. Partner with engineering and product teams to drive resolution of high-priority issues and influence product roadmap based on quality insights
  5. Prepare communication materials and progress tracking for multiple audiences including delivering updates to leadership stakeholders

Skills

Required

  • 8+ years of experience in product operations, business operations, strategy, consulting, or technical program management within a technology or AI-focused organization
  • Analytical experience using data to tell a story and influence product direction using intermediate to advanced SQL
  • Experience building or deploying AI/ML solutions or automation in production workflows
  • Experience communicating and influencing multiple cross-functional stakeholders and senior leadership
  • Experience solving problems and breaking down ambiguous issues into component parts to develop solutions
  • Familiarity with AI or ML product development lifecycles, including model evaluation, safety review, and deployment processes
  • Track record of defining operational strategy in ambiguous, fast-moving environments and driving adoption across large cross-functional organizations
  • Experience building or improving operational tooling such as project tracking systems, workflow automation, or data pipelines to support research or engineering teams
  • Experience working within an AI research, machine learning, or large-scale model development organization

Nice to have

  • Bachelor's degree in a directly related field, or equivalent practical experience

What the JD emphasized

  • AI-powered automation
  • autonomous agents
  • end-to-end workflows
  • product quality
  • operational excellence
  • AI builds
  • quality issues
  • process and tooling improvements
  • product quality
  • AI-driven workflows
  • product area
  • bug triage
  • prioritization
  • classification
  • deduplication
  • resolution
  • quality insights
  • quality trends
  • data-driven decision making
  • evals practice
  • accuracy thresholds
  • LLM judges
  • labelers
  • governance frameworks
  • AI solutions
  • scaling decisions
  • AI workflows
  • data sensitivity guardrails
  • quality signals
  • encryption boundaries
  • privacy-first constraints
  • AI/ML solutions
  • production workflows
  • AI or ML product development lifecycles
  • model evaluation
  • safety review
  • deployment processes
  • AI research
  • machine learning
  • large-scale model development organization

Other signals

  • AI-driven automation
  • autonomous agents
  • end-to-end workflows
  • product quality
  • operational excellence