Sr. Product Manager, Recs Cross-Surface Personalization Los Angeles, California Department

Location

Department

Product Management

Job Type

Full Time

Launched in 2012, Tinder® revolutionized how people meet, growing from 1 match to one billion matches in just two years. This rapid growth demonstrates its ability to fulfill a fundamental human need: real connection. Today, the app has been downloaded over 630 million times, leading to over 97 billion matches, serving approximately 50 million users per month in 190 countries and 45+ languages - a scale unmatched by any other app in the category.

In 2024, Tinder won four Effie Awards for its first‑ever global brand campaign, “It Starts with a Swipe”™

We are looking for a Sr. Product Manager, Recommendations Cross‑Surface Personalization to lead how Tinder’s recommendation system connects with other product surfaces and teams. Tinder’s Recs system powers who members see, when, and why. But true personalization requires coordination not just within Recs, but across the product ecosystem. In this role, you’ll be responsible for making it easier for other Tinder teams to use Recs data, insights, and personalization in their products.

You’ll also ensure the Recs team can support and prioritize requests from other pods, building processes that help everyone work faster and deliver the right solutions. You’ll partner closely with cross pillar teams and Data Science, ML and Recs Engineering, to ensure that Recs data, models, and insights are used consistently and effectively across Tinder. The ideal candidate is strategic and highly cross‑functional, someone who thrives at connecting dots across systems, teams, and goals.

You’ll balance short‑term coordination with long‑term strategy to ensure Recs intelligence is powering every major user touchpoint in a consistent, scalable, and measurable way.

This is a hybrid role and requires in‑office collaboration. This position is based in Palo Alto, CA.

• Expand Recs personalization across surfaces: Define and execute the roadmap for integrating Recs – ranking scores, embeddings, and insights into experiences beyond the main card stack (e.g., discovery, onboarding, post‑match).
• Lead cross‑pod collaboration for Recs: Act as the main point of contact between the Recs org and other Tinder pods (Growth, Revenue, Engagement, etc.). Manage inbound feature and data requests that affect recommendations, ensuring they are evaluated, prioritized, and executed efficiently.
• Build structured intake and prioritization processes: Develop a scalable system for triaging cross‑pod requests – setting clear criteria, ownership, and expected impact. Create transparency around what’s in scope for Recs and how trade‑offs are made.
• Improve feedback loops across pods: Collaborate with partner teams to ensure new experiences send back high‑quality feedback signals (e.g., engagement data, user preferences) that help strengthen Recs models and personalization accuracy.
• Partner with Recs ML and Platform PMs: Align with the Recs ML PM on model capabilities and with the Recs Platform PM on experimentation frameworks to ensure every integration and cross‑pod initiative is measurable and technically sound.

• 6+ years of Product Management experience in large‑scale consumer or marketplace environments.
• Proven success leading cross‑functional or cross‑surface initiatives with multiple dependencies.
• Strong understanding of recommendation systems, personalization, or ML‑driven products.
• Experience defining and managing structured intake or prioritization processes.
• Exceptional communication and stakeholder management skills; able to drive clarity and alignment across diverse teams.
• A systems mindset – you can connect high‑level strategy to detailed execution and build processes that scale.

• Background in large‑scale personalization or ranking systems used across multiple surfaces.
• Familiarity with data platforms, experimentation frameworks, and feedback signal design.
• Experience with marketplace or network‑effect dynamics.
• Track record of improving collaboration between Product, ML, and Recs Engineering teams.

• Flexible Vacation (with no…