Machine Learning Software Engineer (Match Group AI)

What You’ll Do

• Build ML Services: Design and develop backend services and distributed systems that enable the seamless consumption, scaling, and monitoring of ML models.
• Build ML Pipelines: Design and develop ML serving pipelines (real-time and batch) to deliver model outputs with low latency and high reliability.
• Improve Recommendation System: Improve the AI-powered personalized recommendation system to help users find better matches on Match Group’s dating apps (serve better retrieval/ranking/utility models, add better features, etc).
• Co-Engineering with Brands: Engage in deep technical collaboration with engineering counterparts (Tinder, Hinge, Match, Azar, Pairs, etc.) across various global offices, including Seoul, Palo Alto, LA, Vancouver, Dallas, and Tokyo.
 

Required Qualifications

• 2+ years of experience in software engineering, with a focus on Backend, ML Engineering, or Data Engineering.
• Strong understanding of CS Fundamentals (data structures, algorithms, operating systems) and distributed system design.
• Proficiency in at least one modern programming language (e.g., Python, Go, Java, Kotlin, C#) and a "polyglot mindset" to adapt to new stacks quickly.
• A strong interest in how ML models are built and a passion for solving the engineering challenges of deploying them in the real world.
• Proficiency in leveraging AI-powered tools (e.g., Claude Code, Codex, Cursor) to accelerate productivity.
• Professional working proficiency 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): Essential for cross-functional collaboration within the Seoul office.
 

Preferred Qualifications

Experience with the full ML lifecycle, from model training to production deployment.
Experience in building AI-based recommendation system.
Experience in developing scalable backend servers (handling millions of users).
Experience in developing and serving ML-driven services using frameworks such as vLLM, Triton, Ray Serve, or Seldon.
Experience with big data or stream processing frameworks (e.g., Spark, Flink, Kafka) for building robust ML data pipelines.
Experience in collaborating with cross-functional teams and diverse organizations.
Fluent in English (Sophisticated professional interactions with native-level precision and an understanding of cultural nuances.)