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Sr. Product Manager, Recommendations

Job in Palo Alto, Santa Clara County, California, 94306, USA
Listing for: Tinder
Full Time position
Listed on 2026-01-23
Job specializations:
  • IT/Tech
  • Business
Salary/Wage Range or Industry Benchmark: 250000 USD Yearly USD 250000.00 YEAR
Job Description & How to Apply Below

Our Mission

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”™

Our Values One Team, One Dream

We work hand‑in‑hand, building Tinder for our members. We succeed together when we work collaboratively across functions, teams, and time zones, and think outside the box to achieve our company vision and mission.

Own It

We take accountability and strive to make a positive impact in all aspects of our business, through ownership, innovation, and a commitment to excellence.

Never Stop Learning

We cultivate a culture where it’s safe to take risks. We seek out input, share honest feedback, celebrate our wins, and learn from our mistakes in order to continue improving.

Spark Solutions

We’re problem solvers, focusing on how to best move forward when faced with obstacles. We don’t dwell on the past or on the issues at hand, but instead look at how to stay agile and overcome hurdles to achieve our goals.

Embrace Our Differences

We are intentional about building a workplace that reflects the rich diversity of our members. By leveraging different perspectives and other ways of thinking, we build better experiences for our members and our team.

The Role

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.

Where you’ll work

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

In this role, you will:
  • 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…
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