Director of AI (Dating Outcomes)

Join Hinge, a dating app focused on helping people find meaningful relationships. As the Director of AI, you will lead the development of AI systems that drive better matches and outcomes for users. You will set the long-term AI roadmap, partner with other product groups, and ensure responsible AI practices. This is a unique opportunity to work at the intersection of human behavior and machine learning in a high-stakes domain.

• Lead the AI systems at the core of how Hinge connects people, including the recommendation engine, ranking models, and generative capabilities.
• 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.
• Own the AI strategy for dating recommendations across the full journey, from first candidate to last date, and build the frameworks to reason rigorously through ecosystem tradeoffs.
• A degree in Computer Science, Machine Learning, or a related quantitative field. A PhD is a plus, not a requirement
• 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
• 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 track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences
• Prior work in a domain where user outcomes are high-stakes and hard to measure, whether health, education, finance, or something comparably consequential
• External presence through publishing, speaking, or community contribution that reflects genuine intellectual engagement with the field
• A history of deploying Generative AI thoughtfully in user-facing experiences, not as a trend to follow
• 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