Staff Product Manager, Applied Science

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 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 .

Pinterest is where more than half a billion people come to find what they love and build a life around it. That experience depends on deep understanding — understanding what a Pinner loves, what a Pin means, and how to surface the right content at the right moment. Across recommender systems, signals, and representation learning, the Applied Sciences team develops the next generation technologies that power personalized discovery across Pinterest.

We’re looking for Staff Product Managers to help shape the next chapter of Pinterest’s applied AI/ML platform. In these roles, you’ll bring together research ambition and product discipline to help define the future of personalized experiences at one of the world’s leading discovery platforms.

The roles

Applied Sciences at Pinterest spans core areas including representation learning, signals, and recommender systems. This work includes embeddings, shared feature layers, unified models, ranking systems, and behavioral understanding that support retrieval, ranking, ads, and personalized experiences across Pinterest. These are scaled systems and infrastructure that teams across the company depend on, and every innovation compounds to our top line goals.

Pinterest’s work in this space is grounded in a few core principles: using historical user data to understand evolving preferences, developing models and systems that can support multiple tasks and surfaces, and maintaining infrastructure in a cost-conscious way to support sustainable scale across hundreds of millions of users.

As a Staff Product Manager on Applied Sciences, you’ll partner closely with science, engineering, research, and product teams across Pinterest to help define the roadmap, abstractions, and product strategy that make this work broadly useful across the company while strengthening the shared foundation. You’ll help balance innovation, adoption, scale, and long-term platform health.

Learn more about our work: Representation learning: https://labs.pinterest.com/research-and-innovation/representation-learning Recommender systems: https://labs.pinterest.com/research-and-innovation/recommender-systems

What you'll do

Help shape the product vision and roadmap across key areas of Pinterest’s Applied Sciences portfolio, including representation learning, signals, and recommender systems.

Partner with teams across Pinterest to ensure new capabilities are broadly adopted and not built or deployed in silos.

Work closely with science, engineering, research, and surface teams to translate technical roadmaps into product bets with clear success criteria.

Define and communicate how platform value is measured, including adoption, quality, time to production, and infrastructure cost.

Partner across teams to evaluate modeling tradeoffs, support experimentation, and help ensure innovation scales sustainably across Pinterest’s major surfaces.

What we're looking for

7+ years of Product Management experience or equivalent

Demonstrated experience owning a shared ML or AI platform — such as feature stores, embedding platforms, representation learning infrastructure, recommender systems, or equivalent — where the customers are internal engineering and product teams with competing priorities.

Demonstrated success shipping AI/ML products into production on high-traffic consumer surfaces, with direct involvement in retrieval, ranking, recommendation systems, or related areas.

Strong platform product instincts and ecosystem thinking: you understand producers, consumers, and peer programs as stakeholders, and can make prioritization calls that protect the foundation while serving product surfaces.

Technical credibility with ML/AI teams, with the depth to engage substantively on topics like representation learning, experiment design, model architecture tradeoffs, production readiness, and infrastructure tradeoffs.