Job Description

What You'll Do

Frame the Problem: Figure out which problems are worth solving and why, and articulate them in clear, precise language that a diverse team can rally around.
Shape the Roadmap: Decide what matters most, sequence the work, make the tradeoffs, and write PRDs, keeping partners aligned as priorities shift.
Navigate Complex Stakeholder Maps: Act as the connective tissue between Match Group's brands, and the many functions within them, and MG AI engineering. Read the unspoken dynamics, build alignment across brands, functions, cultures, and time zones, and keep everyone moving in the same direction.
Hands-on Execution: Stay deeply involved in the details. Use experiments, behavioral data, and research to guide your calls when signals are imperfect, unblock engineering, and make the small decisions that keep a project moving.

Required Qualifications

Exceptional Communication & Stakeholder Management (most important): You excel at managing stakeholders in complex, high-ambiguity situations. You read between the lines, sense interpersonal and organizational dynamics, and build alignment across teams with competing priorities. This is the single most important quality for this role.
4-6 years of product management experience in the Tech domain, ideally on complex systems or data or ML-driven products.
Fast Learning Curve & Strong Will to Learn: You do not need to be ML-savvy today, but you must ramp quickly and become technically fluent enough to shape direction on model behavior and tradeoffs without needing to be deeply technical.
Fluent in English (Able to conduct business meetings and participate in complex discussions without requiring assistance.)
Fluent in Korean (Sophisticated professional interactions with native-level precision and an understanding of cultural nuances.) Essential for cross-functional collaboration within the Seoul office.
Strong Ownership & Bias for Action: A track record of driving problems to resolution without waiting for permission. You are comfortable operating with ambiguity and making principled calls when the signals are imperfect.
Analytical Rigor: Comfort with data. You can define metrics, design and read experiments, and let evidence guide your decisions.
Global Operational Readiness: Ability to accommodate flexible working hours for regular early morning syncs with US teams (EST/PST).

Preferred Qualifications

A Point of View on AI Tooling: A clear sense of how AI tools can raise the quality and speed of product work, and a habit of using them (e.g., Claude, ChatGPT, Cursor).
ML Product Experience: Exposure to ML-driven products (Recommendation, Search, Ranking, or Trust & Safety) and a working understanding of the ML lifecycle, from data to online A/B testing.
Matching Products Experience: Experience with products that match people or content (e.g., dating, marketplaces, social feeds, or hiring).
Internal Platform / B2B2C Experience: Success in a role where your primary customers were other internal business units or engineering teams.
Cross-functional Collaboration: Experience working with diverse, globally distributed teams and organizations.