Machine Learning Software Engineer at Match Group AI working on recommendation systems and ML infrastructure across dating apps including Tinder and Hinge. Role bridges ML research and production, deploying models handling millions of users with focus on low-latency serving and distributed systems. Hybrid position based in Seoul with global collaboration.
What you need
- 2+ years of software engineering experience in Backend, ML Engineering, or Data Engineering
- Strong understanding of CS fundamentals (data structures, algorithms, OS) and distributed system design
- Proficiency in modern programming language (Python, Go, Java, Kotlin, C#) with polyglot adaptability
- Interest in ML models and passion for deploying them in real-world, high-traffic production environments
- Professional working proficiency in English for business meetings and complex discussions
- Fluent in Korean for sophisticated professional interactions
What you will own
- Design and develop backend services and distributed systems for ML model consumption, scaling, and monitoring
- Build and optimize ML serving pipelines (real-time and batch) for low-latency, high-reliability model delivery
- Improve AI-powered personalized recommendation systems for matching
- Collaborate with engineering teams across global offices (Seoul, Palo Alto, LA, Vancouver, Dallas, Tokyo)
- Deploy state-of-the-art models including LLMs and multimodal systems into production environments
Core skillsPythonGoJavaKotlinC#LLMMultimodal AIDistributed SystemsML PipelinesRecommendation Systems
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
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.
What We're Looking For
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.
Nice to Have
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.)
Hiring Process
Work type: Full-time
Legal Entity: Hyperconnect / Brand: Match Group AI
Recruitment Process: Resume Review > Live Coding Interview > Technical Interview > Technical Deep-dive & Cultural Alignment Interview > Offer
(* Please note that part of the interview process may be conducted in English)
For the document screening stage, only successful candidates will be individually notified of the results.
Application documents: A detailed, career-based English resume (PDF) in free format
This position is open to candidates seeking to transfer as Alternative Military Service Researchers (전문연구요원). For those under this program, service management will be conducted in accordance with the relevant military service exemption laws.
Additional
#mg
#LI-YN1
