Machine Learning Engineer (Match Group AI)

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, and more. Match Group aims to spark meaningful connections for everyone, worldwide, and the MG AI team's role is to apply cutting-edge AI to some of the hardest challenges along that journey, across diverse domains (e.

g., Recommendation, Trust & Safety, Profile Enhancement). Unlike brand-specific teams (e.g., Tinder, HYPERCONNECT AI), the MG AI team offers a unique opportunity to impact the entire Match Group ecosystem. You won't just build for one app; you'll help 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 Machine Learning Engineer at MG AI ML Engineers at MG AI own the model and the metric it optimizes. You turn broad product goals and data into ML problems worth solving, build the models that address them, and measure their impact. Your responsibility covers the decisions around the model as much as the model itself.

You choose the optimization target that translates into business results, design how the training data behind that target is collected and processed, and set the online and offline evaluation criteria that determine whether the model solved the problem. You will work across a wide range of ML problems, and the problems change as the products and the technology do.

Recent examples from the team: Utility modeling: Move beyond event prediction (whether a user will like another user, whether a user will report someone) to model the utility each event delivers to users and to the system, and optimize for the utility of the system as a whole. Cold start and data scarcity: Build models that work when data is missing or thin — prototyping stages, privacy constraints, rare events — and design the loop that collects the data those models need next.

LLM and agentic systems: Leverage LLMs and agentic systems in Recommendation, Trust & Safety, and Profile Enhancement, both as products in their own right and as a way to raise the performance of existing models. Generative model evaluation: Define evaluation metrics for generative models that connect to business outcomes and that the model can be optimized against.

Our engineers also publish selected technical work on the Hyperconnect Tech blog (written in Korean). How to Set ML Objectives How Hyperconnect Built an LLM Explanation Policy LLM-as-a-Judge for Explanation Quality $ - $ a year None LI-YN1 제출해 주신 내용 중 허위 사실이 있거나 관련법 상 근로제공에 결격사유가 있는 경우 채용이 취소될 수 있으며, 필요시 사전에 안내된 채용 절차 외에도 추가 전형 및 서류 확인이 진행될 수 있습니다.

국가보훈대상자는 관계 법령에 따라 우대하오니, 해당되시는 분께서는 지원 시 고지해주시고 채용 시 증빙서류를 제출해주시기 바랍니다. 하이퍼커넥트가 채용하는 포지션에 지원하는 경우, 개인정보 처리에 관하여서는 본 개인정보처리방침이 적용됩니다: https://career.hyperconnect.com/privacy HPCNT