Director of AI, Dating Outcomes
About the Role
Most consumer apps treat your attention as the product, optimizing every recommendation and notification to keep you scrolling. Hinge made the opposite bet: our mission is to get people off the app, out on a date, and eventually to delete us entirely. That is not a tagline. It is the actual constraint every AI system we build has to solve against.
The Director of AI, Dating Outcomes owns the hardest version of that problem. You will lead the AI systems at the core of how Hinge connects people: the recommendation engine, the ranking models, and the generative capabilities that help users put their best selves forward. The goal is not engagement, it is outcomes: fewer ratings per exchange, higher quality matches, and more people finding the person they were looking for. Because Hinge does not sell ads or user data, being exceptional at this is our business model. Reporting to the VP of AI, you will set the long-term AI roadmap for one of the company's most influential product areas and build the teams that bring it to life. The problems sit at the intersection of human behavior, sparse real-world signals, and a two-sided ecosystem of people who are not interchangeable, a different kind of ML challenge than most people in this industry have had the chance to work on.
Responsibilities
Strategy and Vision: Own the AI strategy for dating recommendations across the full journey, from first candidate to last date. Build the frameworks to reason rigorously through ecosystem tradeoffs where a win for one cohort reshapes another, and set a vision ambitious enough that the org wants to chase it.
Cross-Functional and Industry Leadership: Partner closely with other product groups to integrate AI capabilities across the product and shape roadmap decisions you do not own. Communicate honestly with executive leadership on strategy, tradeoffs, and progress. Own budget and investment strategy for the area, and represent Hinge externally through writing, talks, and community.
Technical Leadership: Steer real-time, personalized recommendation systems that reduce ratings per exchange and drive better matches. Bring Generative AI to dating outcomes in ways that are differentiated, authentic, and grounded in how people actually connect.
Responsible AI: Hold the bar on scalability, resilience, and rigorous evaluation for both performance and bias.
Team and Organizational Development: Structure and scale the team for both deep execution and long-horizon bets. Develop emerging leaders, invest in succession across IC and leadership tracks, and build a culture where rigor, inclusion, and performance reinforce one another.
Execution Excellence: Drive predictable delivery, balancing urgent risk response with long-term platform investments. Establish scalable operating rhythms for planning, technical reviews, and incident learnings.
What We're Looking For
10 or more years across data science, applied ML, and software engineering, with 6 or more years in AI or engineering leadership defining strategy, not just managing execution.
A track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences.
Demonstrated success scaling ML or AI teams and developing technical leaders who carry the work over years, not a single launch.
Deep technical fluency across ML, RL, Generative AI, and MLOps, with the communication skills and executive presence to represent AI strategy at the highest levels of the organization.
A degree in Computer Science, Machine Learning, or a related quantitative field. A PhD is a plus, not a requirement.
Experience building AI systems in a two-sided or constrained marketplace, where optimizing for one group shifts outcomes for another and the right answer is rarely obvious.
A history of deploying Generative AI thoughtfully in user-facing experiences, not as a trend to follow.
External presence through publishing, speaking, or community contribution that reflects genuine intellectual engagement with the field.
Prior work in a domain where user outcomes are high-stakes and hard to measure, whether health, education, finance, or something comparably consequential.
What Will Set You Apart