Staff Product Manager, Model Lifecycle & Management
PinterestAbout 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 flexibility 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. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.
As a Senior Product Manager for Signal Lifecycle within Trust & Safety, you'll own the product strategy for the ML platform that powers how Pinterest trains, evaluates, deploys, and measures content safety models at scale. You'll lead the development of ML Signal Management — making ML signals first-class entities with unified metadata and identity across systems. Partnering deeply with ML engineering, data science, content safety, and enforcement systems, you'll drive a platform whose scope is expanding from T&S into content quality, ads safety, and beyond.
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 strateg
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