Staff Product Manager, Model Lifecycle & Management

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the differences in all of us. We give our employees the space to learn, empower them to make choices and create the support they need to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think.

What you'll do:

• Own and drive the Signal Lifecycle product roadmap, including ML Flywheel infrastructure, auto-deployment, model onboarding, golden dataset management, and signal performance measurement
• 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
• Partner with ML Engineering to reduce model iteration time through automated retraining, evaluation, and deployment pipelines
• Own measurement infrastructure — golden dataset strategy, prevalence measurement, model performance dashboards, and experimentation frameworks
• Lead cross-functional signal strategy with Content Safety, Enforcement Systems, Data Science, and Operations

What we're looking for:

• 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