Senior Product Manager, Tinder

• The ideal candidate is strategic and highly cross-functional, someone who thrives at connecting dots across systems, teams, and goals
• 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

Desirable

• 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
• 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
• 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