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Data & AnalyticsHybrid

Machine Learning Software Engineer (Match Group AI)

Match Group · Hybrid

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Last seen by MeritLog September 9, 2026Source: LeverSource version: lever-postings-v1

MeritLog read this listing from Match Group's Lever job board and last checked it on September 9, 2026.

Source: the employer's Lever job board. Open the original listing for current details.

Job details

Work model
Hybrid
Salary
Not listed by source
Location
Seoul, South Korea
Occupation
Data Scientists(O*NET 15-2051.00)

Hiring context

How this role compares at Match Group

Match Group has 73 live roles in MeritLog’s catalog across 10 job families, and 14 of them are in data & analytics. 41 of those listings publish a pay range, a disclosure rate of 56%.

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

Job description

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. Our mission is to solve the most complex challenges in online dating (e.g., Recommendation, Trust & Safety, Profile Enhancement) by bridging cutting-edge AI research with excellent software engineering. 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 MLSE at MG AI While ML Engineers focus on modeling, Machine Learning Software Engineers (MLSEs) at MG AI team focus on the critical bridge between research and production. We ensure that state-of-the-art models (including LLMs and Multimodal systems) are integrated into high-traffic environments, serving millions of users in real-time. We also upload a selection of interesting problems solved by our team's engineers to the Hyperconnect Tech blog (written in Korean). On-device AI Face Verification Pipeline Optimization 10 Python Performance Optimization Tips for High-Performance ML Backends

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