About Match Group AI
Match Group AI is a central technology organization that solves core online dating challenges using AI. We focus on key areas of the user experience—such as profiles, matching, and Trust & Safety—to identify and tackle critical problems. We innovate the user experience by applying the latest AI technologies and data-driven approaches. Furthermore, we expand the group's common technical foundation through collaboration with global dating brands within Match Group, such as Tinder and Hinge.
For more details, please refer to the following article: Introducing the Match Group AI Team.
Responsibilities
An ML Engineer at Match Group AI is both a scientist who researches and applies the latest AI/ML technologies and an engineer who designs and operates models and systems tailored to real-world service environments. Our organization handles the following tasks, and we are looking for individuals who can proficiently perform at least one of them:
- Solve various business problems arising from products serviced by Match Group. This involves understanding the background, goals, and constraints of a business problem, redefining it as a proper AI/ML problem, and executing the process of finding and implementing the best methodology to solve it.
- Participate in the development of new products and features for Match Group. From conceptualization and prototyping to reaching actual users, you will utilize AI/ML technologies to implement products and features quickly and correctly.
To perform these tasks, we expect ML Engineers to have not only a solid foundation in AI/ML but also the adaptability to quickly learn and adopt the latest technologies. Based on these capabilities, you will primarily encounter the following technical topics:
- Methods for correctly handling data across various modalities—such as text, images, and event logs—and resolving biases and noise contained within the data.
- Defining evaluation metrics aligned with product goals and determining the optimal model training methods to achieve those goals.
- Developing Large Language Models (LLMs) and multimodal models specialized for the dating domain and applying them to actual products in forms such as agents.
- Verifying real-world impact through online experiments (A/B testing) and causal analysis, and setting the direction for future improvements.
- Methods for optimizing on-device and large-scale model inference, considering engineering constraints and infrastructure environments.
Requirements
- Possess basic knowledge of AI/ML, deep knowledge in at least one specific domain, and relevant project experience.
- Ability to discover statistical characteristics and patterns in data through Exploratory Data Analysis (EDA) and apply them to the problem-solving process.
- Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX, with the ability to write high-quality, collaborative, and maintainable code.
- Engineering skills required for building and deploying ML model training pipelines.
- Strong interest in the commercialization of AI technology.
- Ability to consistently learn new technologies and share them with the team.
- Ability to communicate fluently in Korean, and the ability to understand/write technical documents in English and communicate remotely.
Preferred Qualifications
- Track record of publications at top-tier AI/ML conferences and journals (e.g., NeurIPS, ICLR, ICML, ACL, RecSys, KDD, CVPR) or awards in related competitions.
- Experience in significantly improving key business metrics by applying AI technology to actual services.
- Ability to uncover meaningful insights from data and apply them to decision-making using statistical techniques and SQL-based analysis, such as A/B test planning, target KPI definition, and causal analysis.
- Experience in project development outside of the AI field, including client-side (Android, iOS) or backend development.
- Experience collaborating with various non-engineering roles (PM, Data, Business, etc.) to define requirements, manage schedules, and adjust priorities at the project or feature level (including PM/PO experience).
- Ability to perform technical communication in English during large meetings or discussions involving multiple stakeholders.
Hiring Process
- Employment Type: Full-time
- Hiring Procedure: Document Screening > Pre-assignment > Live Coding Interview > Technical Interview > In-depth Technical & Cultural Alignment Interview > Final Offer (* The process may be subject to change if necessary.)
- Document screening results will be notified individually to successful candidates.
- Application Documents: Detailed resume based on career history in English (PDF, free format).
- This position is eligible for the Professional Research Personnel (special military service) transfer. For those under special military service, service management will be conducted in accordance with relevant laws.
About the Match Group AI ML Team
The ML Team consists of ML Engineers who apply AI/ML technologies to various Match Group services. The team's work begins with identifying and defining problems that arise during the creation and operation of actual products. We build State-of-the-Art (SotA) models best suited for problem-solving and deploy the completed models stably and efficiently into mobile and server environments. We continue to build the service's AI Flywheel through ongoing monitoring and improvement. In this process, we collaborate closely with various specialized organizations—including backend/frontend/DevOps engineers, data analysts, and PMs—to create AI experiences that make an impact on actual users. For more details on how we work, please refer to the following:
[How AI Lab Works] Head of AI - Shurain Interview
AI in Social Discovery(Blending Research and Production)
Some of our work is shared externally through papers or open-source code. When building ML models for product use, existing research is often insufficient. To fill these gaps, project participants collaborate to refine the results of the research conducted, and if possible, release them along with the code. As a result, we have achieved approximately 20 external research accomplishments, including the following:
- 2024 CUPID: Real-time Session-based Mutual Recommendation System for 1:1 Social Discovery Platforms, presented at ICDM Workshop
- 2023 TiDAL: Active Learning Technique Based on Model Behavior in Efficient Training Processes, published at ICCV 2023
- 2023 Research on setting thresholds to satisfy multiple classification criteria simultaneously in a moderation environment, published at WSDM 2023
- 2022 Research on increasing semantic diversity in dialogue generation, published at EMNLP 2022
- 2022 Method for effective learning in environments with high label noise, published at ECCV 2022
- 2022 Chatbot research mimicking a target character using only a few utterances, published at NAACL 2022
- 2022 Research on improving performance using examples in dialogue generation models, presented at ACL 2022 Workshop
- 2022 Research on distillation technology for audio classification in mobile environments, published at ICASSP
To ensure AI research proceeds effectively, we are well-equipped with infrastructure for deep learning training. To allow ML Engineers to fully develop and experiment with models, we operate a deep learning cluster consisting of 10 AWS-based DGX nodes (8 H100 GPUs per node, 80 H100s in total). We also build and operate our own data pipelines, including data collection and preprocessing, using cloud services, and collaborate with various software engineers (backend/frontend/DevOps/MLSE) to commercialize ML models.
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