Machine Learning Engineer

About Match Group AI

Match Group AI is a centralized technology organization dedicated to solving core challenges in online dating through AI. We focus on key areas of the user experience—such as profiles, matching, and Trust & Safety—to identify and tackle critical problems. By leveraging the latest AI technologies and data-driven approaches, we innovate the user experience. 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

ML Engineers at Match Group AI are scientists who research and apply the latest AI/ML technologies, as well as engineers who design and operate 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 business problems, redefining them into appropriate AI/ML problems, and executing the best methodology to solve them.
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 from various modalities (text, images, event logs, etc.) and resolving biases and noise within the data.
Defining evaluation metrics aligned with product goals and determining 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 actual impact through online experiments (A/B testing) and causal analysis, and setting directions for future improvements.
Methods for optimizing on-device and large-scale model inference, considering engineering constraints and infrastructure environments.

Requirements

Possess fundamental 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 code suitable for collaboration and maintenance.
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

Publication record in top-tier AI/ML conferences and journals (NeurIPS, ICLR, ICML, ACL, RecSys, KDD, CVPR, etc.) or awards in relevant competitions.
Experience in significantly improving key business metrics by applying AI technology to actual services.
Ability to derive meaningful insights from data and apply them to decision-making using statistical techniques (A/B test planning, target KPI definition, causal analysis) and SQL-based analysis.
Experience in project development outside the AI field, including client-side (Android, iOS) or backend development.
Experience in defining requirements, managing schedules, and adjusting priorities for projects or features while collaborating with various roles (PM, data, business, etc.) outside of engineering (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 career-based English resume (PDF) in free format.
This position is eligible for the Professional Research Personnel (Special Military Service) program. For those under special military service, service management will be conducted in accordance with relevant laws.

About the Match Group AI ML Team

Our 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 actual product creation and operation process. We build the most suitable State-of-the-Art (SotA) models for problem-solving and deploy the completed models stably and efficiently to mobile and server environments. We continue to build the AI Flywheel of our services through continuous 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 real users. For more details on how we work, please refer to the following:

[How AI Lab Works] Interview with Head of AI - Shurain

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 (ICDM Workshop)
2023 TiDAL: Active Learning Technique Based on Model Behavior in Efficient Training Processes (ICCV 2023)
2023 Research on Setting Thresholds to Satisfy Multiple Classification Criteria Simultaneously in Moderation Environments (WSDM 2023)
2022 Research on Increasing Semantic Diversity in Dialogue Generation (EMNLP 2022)
2022 Method for Effective Learning in Environments with Heavy Label Noise (ECCV 2022)
2022 Chatbot Research Mimicking a Target Character Using Only a Few Utterances (NAACL 2022)
2022 Research on Improving Performance Using Examples in Dialogue Generation Models (ACL 2022 Workshop)
2022 Research on Distillation Techniques for Audio Classification in Mobile Environments (ICASSP)

For AI research to progress well, infrastructure for deep learning training must be well-equipped. To ensure ML Engineers can sufficiently 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.

If any false information is found in your submission or if there are grounds for disqualification under relevant laws, the offer may be rescinded. Additional screening or document verification may be conducted if necessary beyond the pre-announced hiring process.

Applicants who are eligible for veterans' benefits will be given preference in accordance with relevant laws. Please notify us upon application and submit supporting documents if hired.

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