Hinge is the dating app designed to be deleted
In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all.
About the Role
Every match on Hinge starts with a decision from our recommender: Who should we show you today? Done right, the app feels like it gets you; done wrong, it feels random. This role owns that decision for millions of daters.
As Senior Product Manager on Recommendations, you'll take on challenges like how we welcome new users, how we ensure our recommendations are equitable for all, or how we help people explore as they are figuring things out, and turn them into improvements daters can actually feel. It's hands-on work at the intersection of machine learning and human connection, built shoulder-to-shoulder with data science, ML, engineering, design, and research.
Responsibilities
Own core recs improvements, end to end. Take defined areas from problem to shipped result, and move core user and business metrics.
Make the algorithm feel personal. Partner with data science and ML to improve how we rank and personalize, making the calls on inputs, ranking logic, and experience.
Turn ambiguity into a roadmap. Decide what matters most, sequence the work, make the tradeoffs, and keep partners aligned as things change.
Decide with evidence. Use experiments, behavioral data, and research to guide your calls when the signals are imperfect.
Bring people along. Communicate your plan and results clearly — whether to an engineer in standup or a leader in a review.
Protect trust. Recommendations shape who gets seen. Keep the system fair, safe, and true to Hinge's values.
What We're Looking For
4+ years of product management experience, ideally on personalization, recommendations, ranking, or other ML-powered consumer products.
A track record of owning a product area end to end and driving measurable improvements to core user outcomes at scale.
Comfort partnering closely with data science, ML, and engineering — technically fluent enough to shape direction on model inputs, ranking, and tradeoffs without needing to be deeply technical.
Strong communication skills, especially translating complex ideas for non-technical audiences.
Comfort with ambiguity and making principled calls in high-impact spaces.
Experience with products that match people or content (e.g., dating, marketplaces, social feeds, or hiring).
Curiosity about dating and the human side of product development, and an interest in the fairness, ethics, and inclusivity of algorithmic systems.
A point of view on how AI tools can raise the quality and speed of product work.
