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 Senior MLSE at MG AI
ML Engineers (MLEs) at MG AI own the model and the metric it optimizes. Machine Learning Software Engineers (MLSEs) own what turns that model into a real product: the services and pipelines that serve millions of users, kept fast, reliable, and cost-efficient.
Our problems change as products and AI change, so we care more about how you get things done than about any one tech stack. We are also an AI-native team: almost all of our work is done with AI agents (e.g., Claude Code, Codex).
Recent examples from the team:
- Trust & Safety: Build AI systems that keep users safe at Tinder scale.
- AI APIs for multiple brands: Turn AI features proven on Tinder into shared APIs that other Match Group brands can use.
- New AI experiences: Prototype AI features that help users show their best selves, and take the ones that work to production.
Selected work on our 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
You lead ML projects from a rough goal to a production system that millions of users rely on.
Lead Projects Across Teams: Turn a broad goal into a concrete plan, and keep ML Engineers, PMs, and brand partners across our global offices (Palo Alto, LA, New York, Vancouver, Dallas, Tokyo) aligned. Clear blockers, including ones outside our team.
Design and Build ML Systems: Design backend services and real-time/batch data pipelines that bring ML models to production, from first prototype to millions of users. Work as a real technical partner to ML Engineers.
Serve Models Fast and at Low Cost: Serve deep learning models and large language models with low latency and reasonable GPU cost. Improve throughput with batching, caching, and better use of hardware.
Raise the Bar: Set the example in problem definition, design docs, and AI-driven development, and help other engineers grow.
Required Qualifications
Project Ownership (most important): A track record of leading a complex project from a rough goal to a production system you could stand behind: defining the problem, choosing the technical direction, and owning the outcome. You can explain the trade-offs behind your technical and product decisions.
Engineering Experience: 5+ years of software engineering (backend, ML systems, data engineering, or similar), including building and running services or data pipelines in production.
System Design: You can design scalable, reliable systems and clearly explain the trade-offs.
Hands-on Coding With AI: You write clean, well-tested code (Python preferred), and AI coding agents are part of your daily work.
High Agency and Grit: You find what needs to be done and drive it to a result fast, without waiting for instructions or cutting corners on quality. When you hit a wall, you find another way.
Collaborative and Coachable: You understand what others really need and can persuade them. You take feedback well, act on it, and use AI to learn new things quickly.
Professional Working Proficiency in English and Korean: Able to lead business meetings and take part in complex discussions without help.
Preferred Qualifications
Have a strong base in CS fundamentals, or have worked closely with ML Engineers on how models are trained and served
Have worked as a tech lead or manager
Have built Trust & Safety systems, recommendation system, AI product features, or ML APIs used by multiple teams
Have served ML models (vLLM, Triton, Ray Serve) or built large-scale backends and data pipelines (Spark, Flink, Kafka)
Have turned vague product ideas into something users love, without every detail spelled out
Have worked with teams in other companies, countries, or time zones
Native-level English
Hiring Process
Work type: Full-time
Legal Entity: Hyperconnect / Brand: Match Group AI
Recruitment Process: Resume Review > 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
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