Staff Product Manager, Model Lifecycle & Management

Pinterest Pinterest · Consumer · San Francisco, CA · Trust and Safety Ops

Product Manager for ML Lifecycle & Management focusing on the ML platform for training, evaluating, deploying, and measuring content safety models. Owns product strategy for ML Signal Management, aiming to make ML signals first-class entities with unified metadata and identity across systems. Partners with ML engineering, data science, and content safety teams to drive platform scope expansion.

What you'd actually do

  1. Own and drive the Signal Lifecycle product roadmap, including ML Flywheel infrastructure, auto-deployment, model onboarding, golden dataset management, and signal performance measurement
  2. Define and ship ML Signal Management — a unified backbone that elevates ML signals into first-class entities with comprehensive metadata, cross-system naming, and API access
  3. Partner with ML Engineering to reduce model iteration time through automated retraining, evaluation, and deployment pipelines
  4. Own measurement infrastructure — golden dataset strategy, prevalence measurement, model performance dashboards, and experimentation frameworks
  5. Lead cross-functional signal strategy with Content Safety, Enforcement Systems, Data Science, and Operations

Skills

Required

  • 5+ years product management experience
  • Experience owning or managing ML platforms, model lifecycle infrastructure, or ML tooling
  • Strong data fluency — comfortable with precision/recall/FPR, evaluation methodology, and model performance measurement
  • SQL proficiency — able to self-serve data investigation and analysis
  • Demonstrated systems thinking — experience with complex interconnected infrastructure serving multiple teams
  • Strong cross-functional leadership — proven ability to drive decisions across ML engineering, data science, and product stakeholders
  • Excellent written and verbal communication of complex ML and infrastructure concepts
  • Bachelor’s degree in a relevant field such as Computer Science, or equivalent experience

What the JD emphasized

  • ML platforms
  • model lifecycle infrastructure
  • ML tooling
  • model performance measurement
  • complex interconnected infrastructure

Other signals

  • ML platform
  • model lifecycle
  • content safety models
  • ML Signal Management
  • automated retraining, evaluation, and deployment