Match Group
Product Manager (Match Group AI)
Seoul, South Korea
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hirly's read of this role
- Role family
- Product management
- Seniority
- Lead / management
- Country
- KR
- Work mode
- On-site / unstated
- First seen by hirly
- 21 Sept 2026
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the posting
Match Group AI Team Introduction
Match Group AI (MG AI) is the central tech organization that drives innovation across Match Group's global portfolio, including Tinder, Hinge, Azar, Pairs, Match, BLK, etc . Match Group aims to spark meaningful connections for everyone, worldwide, and the MG AI team's role is to bring cutting-edge AI to unblock the challenges along that journey, across diverse domains (e.g., Recommendation, Trust & Safety, Profile Enhancement).
Unlike brand-specific teams (e.g., Tinder, HYPERCONNECT AI), MG AI team offers the unique opportunity to impact the entire Match Group ecosystem. You won't just build for one app; we aim to develop scalable AI solutions that power Tinder, Hinge, and beyond, defining the technological gold standard for the global dating industry.
Detailed article: Introduction to Match Group AI Team (written in Korean)
Working as a Product Manager at MG AI
While engineers focus on building and shipping models, Product Managers at MG AI own the why and the what. We translate complex, often ambiguous business challenges from Match Group's brands into clear ML product strategy, and we drive execution across a globally distributed team. The PMs who thrive here are strategic troubleshooters with a bias for action: they spot what is blocking progress and drive it to resolution instead of waiting.
This is a communication-first role, but that does not mean coordination alone. Your core job is to figure out which problem is worth solving, articulate it in clear and precise language, and drive alignment among a diverse set of stakeholders across cultures and time zones. You own the product thinking end to end: framing the problem, shaping the plan, and defining what we build.
You will work on projects that leverage a wide range of technologies, from on-device ML modules to server-side AI models and LLMs. You do not need to be an ML expert on day one, but you must learn fast and become technically fluent enough to make sound product calls, because the technology and the problems keep changing.
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