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[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 ML, Azar 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.
[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.
• Shape the Problem: You are given the goal in broad terms. You make it concrete: what problem the project actually solves, what falls outside it, and what technical direction it takes.
• Define What Success Means: Translate the goal into a metric a model can optimize, and define the online and offline evaluation criteria that determine whether the target metric improved.
• Design the Model and the Data Behind It: Choose the modeling approach that fits the problem and the metric you are optimizing. Design how training data is collected, processed, and fed back, so that user behavior in production becomes the data that improves the next model.
• Drive Execution Across Teams: Align engineers, product partners, and brand counterparts across Match Group's global offices — Seoul, Palo Alto, LA, Vancouver, Dallas, Tokyo — around a shared plan. Keep partners outside your project team aligned, and resolve the blockers that arise outside the team.
• Depth and Breadth in AI/ML: Broad knowledge across multiple AI/ML domains, with deep expertise in at least one. A PhD in a related field, 5+ years of industry experience, or equivalent capability.
• Problem Definition and Technical Judgment: You turn broad business and technical goals into ML problems that can be solved and measured, and you reach sound technical decisions under incomplete information.
• Data Literacy: You use exploratory data analysis to understand what the data can and cannot support, and let what you find shape the problem you take on.
• ML Frameworks and Tooling: Proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX, with the engineering judgment to design, build, and evolve the training, evaluation, and serving systems around production models.
• Cross-Functional Leadership: Experience leading projects that span multiple functions, and working with stakeholders and leadership outside the project team.
• 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.
• Global Operational Readiness: Ability to accommodate flexible working hours for regular early morning syncs with US teams (ET/PT).
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 ML, Azar 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.
[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.
주요업무
You lead complex ML projects end-to-end. Cross-functional teams form around the work you drive, and you carry it from a rough goal through launch to a measurable outcome.• Shape the Problem: You are given the goal in broad terms. You make it concrete: what problem the project actually solves, what falls outside it, and what technical direction it takes.
• Define What Success Means: Translate the goal into a metric a model can optimize, and define the online and offline evaluation criteria that determine whether the target metric improved.
• Design the Model and the Data Behind It: Choose the modeling approach that fits the problem and the metric you are optimizing. Design how training data is collected, processed, and fed back, so that user behavior in production becomes the data that improves the next model.
• Drive Execution Across Teams: Align engineers, product partners, and brand counterparts across Match Group's global offices — Seoul, Palo Alto, LA, Vancouver, Dallas, Tokyo — around a shared plan. Keep partners outside your project team aligned, and resolve the blockers that arise outside the team.
자격요건
• Project Ownership (most important): A track record of leading a complex ML project from a rough goal to a result you could stand behind — defining the problem, choosing the technical direction, and owning the outcome. You have made consequential technical and product decisions and can explain the trade-offs involved.• Depth and Breadth in AI/ML: Broad knowledge across multiple AI/ML domains, with deep expertise in at least one. A PhD in a related field, 5+ years of industry experience, or equivalent capability.
• Problem Definition and Technical Judgment: You turn broad business and technical goals into ML problems that can be solved and measured, and you reach sound technical decisions under incomplete information.
• Data Literacy: You use exploratory data analysis to understand what the data can and cannot support, and let what you find shape the problem you take on.
• ML Frameworks and Tooling: Proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX, with the engineering judgment to design, build, and evolve the training, evaluation, and serving systems around production models.
• Cross-Functional Leadership: Experience leading projects that span multiple functions, and working with stakeholders and leadership outside the project team.
• 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.
• Global Operational Readiness: Ability to accommodate flexible working hours for regular early morning syncs with US teams (ET/PT).
