Principal Product Manager, Matching & Recommendations

At Bumble, we’re building a world where all relationships are healthy and equitable- and Matching is where that mission comes to life. Every swipe, recommendation, and connection is powered by the systems you will help shape. As a Principal Product Manager, you will lead the evolution of how people discover each other across our ecosystem, starting with Bumble Date and expanding across BFF, Bee and Badoo.

This role sits at the intersection of product, machine learning, and platform strategy. You will define and drive a unified vision for Matching that spans three critical layers: the underlying platform that powers it, the models and ranking systems that personalise it, and the user experiences that bring it to life. Your work will directly elevate match quality, trust, and meaningful outcomes for millions of people.

Operating with a high degree of ownership, you will partner across engineering, data science, design, and Trust & Safety to reimagine Matching as an intelligent, adaptive system. You’ll champion Bumble values like Curiosity and Courage -asking bold questions, exploring new approaches, and continuously improving how we serve our global community. AI will be central to your work, from shaping model-driven experiences to ensuring responsible, inclusive, and transparent use of intelligent systems.

### What you’ll do

• Define and drive the long-term product vision and strategy for Matching & Recommendations, aligning with company-wide goals and delivering measurable impact on engagement, quality of matches, and user trust
• Lead the development of scalable recommendation systems, including ranking algorithms, personalisation models, and relevance optimisation
• Partner closely with Data Science and Engineering to design, ship, and iterate on machine learning-powered features, embedding AI into core product experiences in a thoughtful and responsible way
• Own experimentation strategy end-to-end, including hypothesis development, A/B testing at scale, and translating insights into product decisions
• Use data fluently to identify opportunities, define success metrics (e.g. match quality, retention, conversion), and guide prioritisation across complex problem spaces
• Collaborate cross-functionally with Trust & Safety to ensure recommendations are safe, inclusive, and aligned with Bumble’s values of Respect and Excellence
• Take ownership of delivery across multiple teams, working collaboratively and adapting plans with an agile mindset to drive outcomes in a fast-moving environment
• Influence senior stakeholders and align cross-functional teams around a shared vision, seeing initiatives through from insight to impact

### About you

• Deep experience in product management within consumer technology, ideally working on personalisation, discovery, or recommendation systems
• Strong understanding of machine learning concepts and their application in product development (e.g. ranking systems, recommendation models, experimentation frameworks)
• Proven track record of leading complex, high-impact product areas with significant autonomy and cross-functional influence
• Exceptional analytical skills, with experience using data to inform decisions, define metrics, and evaluate product performance at scale
• Ability to collaborate with purpose across engineering, data science, and design, while taking ownership of outcomes and driving alignment across stakeholders
• Demonstrated agile mindset—comfortable navigating ambiguity, iterating quickly, and continuously improving products and processes
• Role model Bumble values such as Curiosity and Courage, bringing thoughtful challenge, inclusive thinking, and a commitment to building respectful, user-centric experiences
• Typically requires 12–15 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
• Strong AI fluency, with the ability to independently leverage AI tools and partner with technical teams to shape AI-powered product experiences, while considering ethics, bias, and user impact