Match Group

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

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. Program Duration The internship program will run from June 1 through August 31, 2027. Where you’ll work This is a hybrid role that requires in-office collaboration three days per week in Palo Alto, California. About the Role The Tinder ML team drives impact across nearly every core domain of the product — from Recommendations and Trust & Safety to Profile, Chat, Growth, and Revenue. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem. With Tinder's global scale and impact, you'll be at the forefront of solving some of the most complex challenges in technology. In this internship program, you will collaborate with senior engineers to build and deploy ML solutions that advance Tinder’s business goals. What you'll do: Gain real-world experience on exciting challenges to improve the user experience. Design and implement machine learning algorithms for the assigned domains: Recommendation, Trust, Profile, Chat, Growth, or Revenue. Experience the ML model formulation of production problem under the guidance. Work closely with a Senior Engineers to develop machine learning solutions to further Tinder’s business goals. Participate in the annual Tindership Hackathon, presenting with your team to the entire Tinder team and panel of executives. What we're looking for: Aspiring ML Engineer who’s excited to work on large scale challenges with cutting-edge technology. Practical knowledge of how to build efficient end-to-end ML workflows. Proficient in Python. Hands-on experience in designing and building ML models. Foundational knowledge of basic Computer Science principles: data structures and algorithms. Currently pursuing a BS/BA or MS in Computer Science or a related field. Nice to have: Publications in top ML or data science conferences (e.g., NeurIPS, ICML, RecSys, KDD). Experience deploying ML models in production environments. Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or Keras. Experience with Databricks, Spark, or Airflow. Proficiency in additional programming languages like Go, Java or Scala.

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. Program Duration The internship program will run from June 1 through August 31, 2027. Where you’ll work This is a hybrid role that requires in-office collaboration three days per week in San Francisco. About the Role As a Data Scientist Intern at Tinder, you’ll help us uncover insights from one of the world’s largest datasets to enhance user experience, strengthen safety, and drive innovation across our platform. You’ll work alongside talented engineers, data scientists, and product teams to solve real-world problems using data-driven approaches that shape the future of how people connect. What you'll do: Design and execute A/B experiments to measure the impact of product features and algorithmic changes. Help build and improve the team’s experimentation and analytics tooling, including AI-assisted workflows, with a focus on standardization, automation, and clear documentation. Take a scoped analysis from question to recommendation, with guidance from your mentor. Collaborate with cross-functional partners in Data Science, Product, and Engineering to translate insights into actionable strategies. Present key findings and project outcomes to Product and Leadership teams during end-of-internship demos. Translate data insights into clear narratives and recommendations that inform product roadmaps and user experience improvements. Explore and analyze large-scale datasets to identify patterns, trends, and opportunities. What we're looking for: Passion for using data and experimentation to solve complex problems. Intermediate to advanced SQL skills for data querying and analysis. Familiarity with / building to proficiency in Python. Strong foundation in statistics, probability, and experimental design (A/B testing, hypothesis testing). Clear written communication and a curious, fast-learning mindset. Comfort using AI tools (e.g., LLM-based coding and analysis assistants) as a regular part of your workflow, and the judgment to check their output. Currently pursuing a BS/BA, MS, or PhD in Computer Science, Data Science, Economics, Statistics, Applied Math, or a related field.

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. Where you’ll work: This is a hybrid role and requires in-office collaboration in Ghent three times per week. The Team or Role: As a Data Scientist Intern on Tinder’s Modes team in Ghent, you’ll help build new ways for people to meet on Tinder, from group and shared experiences to new modes of using the app. You’ll work with product, engineering, and data science partners to understand how members use these experiences, define how we measure success, and turn insights into product decisions. In this role, you will: Design and execute A/B experiments to measure the impact of product features and algorithmic changes. Help build and improve the team’s experimentation and analytics tooling, including AI-assisted workflows, with a focus on standardization, automation, and clear documentation. Take a scoped analysis from question to recommendation, with guidance from your mentor. Collaborate with cross-functional partners in Data Science, Product, and Engineering to translate insights into actionable strategies. Present key findings and project outcomes to Product and Leadership teams during end-of-internship demos. Translate data insights into clear narratives and recommendations that inform product roadmaps and user experience improvements. Explore and analyze large-scale datasets to identify patterns, trends, and opportunities. You’ll Need: Passion for using data and experimentation to solve complex problems. Intermediate to advanced SQL skills for data querying and analysis. Familiarity with / building to proficiency in Python. Strong foundation in statistics, probability, and experimental design (A/B testing, hypothesis testing). Clear written communication and a curious, fast-learning mindset. Comfort using AI tools (e.g., LLM-based coding and analysis assistants) as a regular part of your workflow, and the judgment to check their output. Currently pursuing a BS/BA, MS, or PhD in Computer Science, Data Science, Economics, Statistics, Applied Math, or a related field.

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. The Team or Role Agents now do real work at Tinder. They review code, security risks, accessibility, make UI improvements, help marketing teams with their workflows, and run inside engineers' coding tools and in Slack. Every one of them reaches our internal systems through our agent platform: a gateway, a growing set of MCP connectors, and the identity, policy and observability layers around them. We're hiring the engineer who owns that platform's design and functions by closely collaborating with internal teams on real use cases. You'll make it safe by default for agents that can read sensitive data and take consequential actions, fast and reliable enough that teams want to build on it, and simple enough that the next connector or agent takes days, not weeks. Where you’ll work: This is a hybrid role and requires in-office collaboration three times per week in our Palo Alto, California office. In this role, you will: Work closely with internal teams to build real solutions for challenges they are facing day to day. Own the design of the agent platform across gateway, connectors, identity, policy, guardrails, observability and cost, and write the roadmap for how it evolves. Design identity and delegation for agents: acting on behalf of a user, agent-owned identities for unattended jobs, short-lived scoped credentials, and every action traceable to a responsible human. Build access control beyond tool allowlists: per-action, per-argument and per-data-class policy, human approval for irreversible actions, and policy that's reviewable and tested. Build guardrails enforced at the tool boundary, not in prompts, against prompt injection through tool output, confused-deputy access, data exfiltration and over-broad scopes. Make agent behavior observable: traces of tool calls and decisions, audit trails investigators can use, evals that catch regressions when models or prompts change, and per-team cost attribution. Set the bar for connectors and third-party MCP servers: onboarding, review, versioning, sandboxing and safe defaults. Run the platform as production and own its on-call: latency budgets, failure modes, rate limits and quotas, kill switches and incident response. Partner with Security, our forward deployed engineers and platform teams, and mentor the engineers who build on and contribute to the platform. You'll need: Great communication and collaboration skills, experience working with real challenges of customer 8+ years building backend, infrastructure or platform systems that other teams depend on, with recent hands-on work on LLM agents, tool use or MCP. Experience shipping identity or authorization systems: OAuth 2.0 and OIDC, token exchange or delegation, service identity, and policy models (RBAC, ABAC or relationship-based). An attacker's mindset about agent systems, and the ability to explain why prompt-level defenses aren't enough. Experience operating gateways, proxies or other services in the critical request path, including on-call, observability and incident response. Strong skills in Python or TypeScript, and daily use of AI coding tools with a clear sense of where they fail. A track record of working well with security teams and impatient internal customers, turning "no" into a safe "yes, like this." Nice to have: Experience with policy engines or workload identity. Built or operated an LLM gateway, model router or MCP server in production. Familiarity with OpenTelemetry and tracing for non-deterministic systems. Contributions to agent identity or MCP authorization standards. Engineering background in application protection, identity and access management, or threat detection and incident response.

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. The Role The Senior Manager, Enterprise AI Engineering will lead a team focused on turning rapidly evolving AI capabilities into practical solutions for teams across Match Group. You will partner with functions including Finance, Legal, People, Marketing, Communications, and Operations to identify high-value opportunities, rapidly build and deploy AI-powered solutions, and scale what works across the organization. This is a hands-on leadership role for a builder who can move comfortably between business problems and technical solutions. You’ll work with functional leaders, domain experts, Engineering, Data, IT, Security, and Privacy to take ideas from prototypes to reliable, reusable enterprise capabilities. What You'll Do Lead and grow a team of AI engineers focused on delivering high-impact enterprise solutions across the organization. Build and own AI infrastructure Lead design and development of AI powered tools, agents, retrieval systems, automations, and services Own and evolve core AI architecture - LLM Gateway, MCP Gateway, native and custom integrations, centralized app and dashboard deployment, and model access management Shepherd solutions from prototype through production in close partnership with Engineering, Data, IT, Legal, Security, and Privacy Drive adoption and standardization Partner with brand AI leads and engineering leads across teams to establish shared infrastructure and avoid duplicated effort Define engineering practices and technical standards for building reliable, secure, and scalable AI applications Connect related AI initiatives and actively share technology and learnings across teams Evaluate and expand capabilities Evaluate, pilot, and roll out new models, tools, and platform feature in collaboration with legal and Security Build frameworks to measure quality, reliability, adoption, and business impact Be the connective tissue Manage vendor relationships and internal stakeholder expectations Partner with functional leaders and domain experts to identify and prioritize high-impact AI opportunities and ensure solutions are grounded in real business needs Who You Are A hands-on technical leader who enjoys building as much as leading. Experienced with modern AI technologies, including LLMs, agents, RAG, evaluations, model selection, and workflow automation. Comfortable with APIs, data, integrations, cloud architectures, security, and enterprise systems. Able to translate ambiguous business problems into practical technical solutions. Pragmatic and outcome-oriented. You care about solving meaningful problems rather than using AI for its own sake. Effective at building trust and driving complex initiatives across technical, business, and executive stakeholders. Curious, adaptable, and comfortable operating in a field where capabilities and best practices evolve rapidly. Qualifications Required 5+ years of relevant engineering and management experience , including meaningful hands-on experience building and deploying AI-powered products or systems and managing teams of engineers. Experience building applications using modern generative AI technologies, including LLMs, agentic workflows, RAG, evaluations, and model APIs. Strong software engineering fundamentals and experience building production systems, APIs, integrations, and data-driven applications. Experience taking AI or emerging technologies from prototype through production and adoption. Experience leading engineers and complex, cross-functional technical initiatives. Demonstrated ability to work directly with business stakeholders and domain experts to translate problems into working solutions. Excellent communication skills across technical, business, and executive audiences. Preferred Experience building enterprise AI products or platforms used across multiple business functions. Experience with enterprise AI architecture, cloud infrastructure, security, data integration, and governance. Experience designing evaluation and monitoring frameworks for generative AI systems. Experience creating reusable platforms, services, or components from initially specialized use cases. Experience scaling emerging technologies within a large organization.

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. We are seeking a talented and motivated early-career software engineer to join the Match Group Core in a brand new role enabling greater productivity in non-technical teams through bespoke software and workflow solutions. Match Group Core is the central services division for Match Group, providing shared services and data solutions for all brands under the Match Group portfolio, including Tinder, Hinge, Match.com, POF, OKC, and many others. Additionally, Match Group Core is a technical service provider for many internal central and corporate teams, such as HR, Finance, Accounting, Legal, Privacy, Marketing, IT, and Security. With a special focus on internal product development and workflow enablement, you'll play a crucial role in designing, building, and maintaining internal products and workflow solutions (HR/People-focused) that benefit employees across the organisation. Most crucially, you will be enabled with extensive agentic tooling to achieve your goals, and in turn, you will be expected to impart your expertise on your stakeholders, and enable them with AI to achieve their own goals. This role is a paid, fixed-term 4-6 month internship, with an opportunity that this role can be converted into a full-time permanent position at the conclusion. This is also a hybrid role, and requires in-office collaboration three times per week. In this role, you will: In this role, you will: Partner directly with corporate teams (primarily HR) to understand their problems and ship working internal tools quickly: dashboards, portals, small web apps, reports, and integrations. Use AI-assisted development tools (Claude Code, Cursor, Codex, etc) as your primary means of building, while applying engineering judgement to what those tools produce. Administer, optimize, and expand a multi-tenanted Databricks platform to meet evolving analytics and business intelligence requirements. Teach and coach non-technical stakeholders to build and maintain their own AI-assisted workflows and tools, which may include running informal group sessions and pairing sessions, or producing guidance, templates, or sufficient guardrails that they can leverage or re-use. Develop and deliver solutions according to lightweight but sensible software development methodologies, with close collaboration MG Core's product engineering arm, to ensure what is built is secure and maintainable. Create and maintain clear documentation, including technical notes, integration docs, and operational and how-to guides written for a non-technical audience. Monitor and troubleshoot the tools you ship, providing timely fixes and keeping stakeholders informed. Keep up to date with the fast-moving AI tooling landscape and bring the best of it back to the team. Gain broad exposure across Match Group’s central services and corporate functions. You'll need: 0-2 years of professional experience as a software engineer, or a recent degree, diploma, or bootcamp qualification in a relevant field. Hands-on experience using agentic development harnesses and similar AI tools (Claude Code, Cursor, Codex, etc), with experience or familiarity with agentic instructions ( AGENTS.md , skills) and MCP servers. A strong understanding of foundational software principles and programming fundamentals, as well as development lifecycles, spanning source control (GitHub), CI/CD (automated testing and quality analysis), and monitoring (logs, metrics, error detection and troubleshooting). Familiarity with software delivery methodologies, such as agile scrum or kanban. Experience in building modern web frontends (using React or similar), as well as accompanying backend REST APIs. Familiarity with relational/SQL, as well as NoSQL or KV databases. Working knowledge of cloud platforms (such as AWS, GCP, Azure, Cloudflare), including conceptual understanding of serverless concepts. Strong interpersonal and communication skills - a significant part of this role is explaining technical work to non-technical people, so patience, empathy, and clarity matter as much as coding ability. Solid understanding of IT security concepts, as well as sound judgement around data sensitivity and confidentiality - much of what you build will touch employee and business data. Excellent problem-solving skills and a bias toward delivering value quickly rather than getting it perfect - as is the nature of internal tooling. Strong enthusiasm for software development and eagerness to learn and grow. Legally authorized to work in Canada for the duration of the internship. Nice to have: Any exposure to HRIS platforms, especially Workday - even light experience as a user or administrator of a HRIS or similar is valuable here. Experience in the Human Resources domain, or with people data, recruiting, payroll, or benefits systems. Experience working within specific cloud environments, namely Cloudflare (primary) and AWS (secondary). Experience working with serverless hosting technologies, such as Cloudflare Workers. Experience working within containerised environments, such as Docker and/or DevContainers. Exposure or experience working within project management tools such as Jira or Monday.com. Experience contributing to open source projects. Experience working at a B2B or SaaS company.

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. The Team or Role: Match Group’s Analytics Team is looking for a Data Scientist II with expertise in Marketing Analytics. Our analytics team serves the entire MG portfolio, acting as a center of excellence to standardize how we measure marketing performance across all our global brands, including industry leading names like Tinder and Hinge. We develop a deep understanding of the business and its data and establish close working relationships with all teams including Product, Marketing, Engineering, and Finance, so that we can provide recommendations and make proposals regarding prioritization of initiatives, marketing investments, A/B tests, and company performance. As a data scientist in this role, you will partner with Marketing teams at each of our BUs, providing support through advanced marketing measurement techniques, A/B test design and analysis, and data-driven insights through ad-hoc analyses, presentations, and automated reports and dashboards. This role will require extensive experience with SQL, data wrangling, statistics, econometric modeling, and A/B testing, as well as the ability to work in cross-functional teams to drive actionable insights. Experience with user growth and acquisition across multi-brand consumer products is a plus. Where you’ll work: This is a hybrid role and requires in-office collaboration three days per week. This position is located in NYC or Los Angeles. In this role, you will: Master and be responsible for analyzing marketing and business performance. Build models better to understand the impact of our marketing investments and strategy Find the “story” in the data and communicate insights and opportunities with executives and other internal teams. Perform ongoing ad-hoc analyses to evaluate the efficiency of our marketing spend, build reporting infrastructure, and assist in making budgeting recommendations to optimize growth. Integrate captured event data with other customer data sources, such as surveys, ad publishers, and app store data. Support reporting and business intelligence needs of the entire Marketing team. You’ll need: 3+ years of relevant experience in analytics or data science Experience building bayesian regressions and/or other marketing measurement techniques Experience designing, executing, and measuring geo experiments and causal inference techniques Ability to think objectively and interpret meaningful themes from quantitative and qualitative data Ability to build constructive and effective relationships with a broad and diverse group of business partners Ability to execute complex analytics projects where you have to determine salient questions in a complex ecosystem and present action-oriented conclusions Proficiency with SQL required Scripting skills, preferably in Python. Knowledge of statistics, specifically experimental design and analysis Proficiency in Microsoft Excel and Microsoft PowerPoint (or equivalents) Familiarity with common marketing KPIs, metrics, and growth experiments Differentiators: Curiosity about how things work, from both a business and technical perspective, leading to the identification and exploration of new strategic opportunities, potential improvements to our data infrastructure, and deepening our understanding of member behavior. Proactive collaborator skilled in engaging with cross-functional teams (Marketing, Product, Engineering, Machine Learning) to influence decision-making, confidently voicing concerns and alternative perspectives Ability to prioritize time across multiple projects and communicate timelines

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 What You'll Do You own ML projects end-to-end. You work alongside other ML Engineers and carry projects from a rough goal through launch and measurement. 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. Collaborate Across Teams: Work with engineers, product partners, and brand counterparts across Match Group's global offices — Seoul, Palo Alto, LA, Vancouver, Dallas, Tokyo. Keep partners outside your project team informed, and surface blockers early enough for the team to address them. Required Qualifications Project Ownership (most important): Demonstrated experience owning an ML project end-to-end, including planning and coordinating the work required to deliver a measurable result. Depth in AI/ML: A solid foundation in AI/ML with deep knowledge in at least one domain, backed by project experience in which you applied it. Problem Definition and Technical Judgment: You turn a business and technical goal into an ML problem that can be solved and measured, and you make reasonable technical choices when the information is incomplete. Data Literacy: You use exploratory data analysis to find the statistical properties and patterns in the data, and let what you find shape how you approach the problem. ML Frameworks and Tooling: Proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX, and the ability to pick up new AI/ML tools quickly as the field moves. Cross-Functional Collaboration: Experience working on projects with people from other functions, and the ability to keep stakeholders outside the project aligned on its progress, key decisions, and risks. 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). Preferred Qualifications Research Track Record: Publications at top-tier AI/ML venues (NeurIPS, ICLR, ICML, ACL, RecSys, KDD, CVPR) or awards in related competitions. Problem Discovery and Initiative: Experience identifying a problem or research direction on your own, without one being handed to you, and carrying it through to a paper or a business result. Experimentation and Causal Analysis: Designing A/B tests, defining target KPIs, and using causal analysis and SQL-based analysis to turn data into decisions. ML Systems Engineering: Software engineering capability to build the AI/ML systems around your models, such as training pipelines and serving systems. Leading Projects Across Functions: Experience leading a project that spans multiple functions, and working with stakeholders and leadership outside the project team. Fluent in English: Sophisticated professional interactions with native-level precision and an understanding of cultural nuances. Hiring Process Work type: Full-time Legal Entity: Hyperconnect / Brand: Match Group AI Recruitment Process: Resume Review > Live Coding Interview > Hiring Manager Interview > 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 This position is open to candidates seeking to transfer as Alternative Military Service Researchers (전문연구요원). For those under this program, service management will be conducted in accordance with the relevant military service exemption laws.

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. Match Group aims to spark meaningful connections for everyone, worldwide, and the MG AI team's role is to bring cutting-edge AI to unblock the challenges along that journey, across diverse domains (e.g., Recommendation, Trust & Safety, Profile Enhancement). 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 Product Manager at MG AI While engineers focus on building and shipping models, Product Managers at MG AI own the why and the what. We translate complex, often ambiguous business challenges from Match Group's brands into clear ML product strategy, and we drive execution across a globally distributed team. The PMs who thrive here are strategic troubleshooters with a bias for action: they spot what is blocking progress and drive it to resolution instead of waiting. This is a communication-first role, but that does not mean coordination alone. Your core job is to figure out which problem is worth solving, articulate it in clear and precise language, and drive alignment among a diverse set of stakeholders across cultures and time zones. You own the product thinking end to end: framing the problem, shaping the plan, and defining what we build. You will work on projects that leverage a wide range of technologies, from on-device ML modules to server-side AI models and LLMs. You do not need to be an ML expert on day one, but you must learn fast and become technically fluent enough to make sound product calls, because the technology and the problems keep changing. What You'll Do Frame the Problem: Figure out which problems are worth solving and why, and articulate them in clear, precise language that a diverse team can rally around. Shape the Roadmap: Decide what matters most, sequence the work, make the tradeoffs, and write PRDs, keeping partners aligned as priorities shift. Navigate Complex Stakeholder Maps: Act as the connective tissue between Match Group's brands, and the many functions within them, and MG AI engineering. Read the unspoken dynamics, build alignment across brands, functions, cultures, and time zones, and keep everyone moving in the same direction. Hands-on Execution: Stay deeply involved in the details. Use experiments, behavioral data, and research to guide your calls when signals are imperfect, unblock engineering, and make the small decisions that keep a project moving. Required Qualifications Exceptional Communication & Stakeholder Management (most important): You excel at managing stakeholders in complex, high-ambiguity situations. You read between the lines, sense interpersonal and organizational dynamics, and build alignment across teams with competing priorities. This is the single most important quality for this role. 4-6 years of product management experience in the Tech domain, ideally on complex systems or data or ML-driven products. Fast Learning Curve & Strong Will to Learn: You do not need to be ML-savvy today, but you must ramp quickly and become technically fluent enough to shape direction on model behavior and tradeoffs without needing to be deeply technical. Fluent 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 and an understanding of cultural nuances.) Essential for cross-functional collaboration within the Seoul office. Strong Ownership & Bias for Action: A track record of driving problems to resolution without waiting for permission. You are comfortable operating with ambiguity and making principled calls when the signals are imperfect. Analytical Rigor: Comfort with data. You can define metrics, design and read experiments, and let evidence guide your decisions. Global Operational Readiness: Ability to accommodate flexible working hours for regular early morning syncs with US teams (EST/PST). Preferred Qualifications A Point of View on AI Tooling: A clear sense of how AI tools can raise the quality and speed of product work, and a habit of using them (e.g., Claude, ChatGPT, Cursor). ML Product Experience: Exposure to ML-driven products (Recommendation, Search, Ranking, or Trust & Safety) and a working understanding of the ML lifecycle, from data to online A/B testing. Matching Products Experience: Experience with products that match people or content (e.g., dating, marketplaces, social feeds, or hiring). Internal Platform / B2B2C Experience: Success in a role where your primary customers were other internal business units or engineering teams. Cross-functional Collaboration: Experience working with diverse, globally distributed teams and organizations.

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 MLSE at MG AI While ML Engineers focus on modeling, Machine Learning Software Engineers (MLSEs) at MG AI team focus on the critical bridge between research and production. We ensure that state-of-the-art models (including LLMs and Multimodal systems) are integrated into high-traffic environments, serving millions of users in real-time. We also upload a selection of interesting problems solved by our team's engineers to the Hyperconnect 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 Build ML Services: Design and develop backend services and distributed systems that enable the seamless consumption, scaling, and monitoring of ML models. Build ML Pipelines: Design and develop ML serving pipelines (real-time and batch) to deliver model outputs with low latency and high reliability. Improve Recommendation System: Improve the AI-powered personalized recommendation system to help users find better matches on Match Group’s dating apps (serve better retrieval/ranking/utility models, add better features, etc). Co-Engineering with Brands: Engage in deep technical collaboration with engineering counterparts (Tinder, Hinge, Match, Azar, Pairs, etc.) across various global offices, including Seoul, Palo Alto, LA, Vancouver, Dallas, and Tokyo. Required Qualifications 2+ years of experience in software engineering, with a focus on Backend, ML Engineering, or Data Engineering. Strong understanding of CS Fundamentals (data structures, algorithms, operating systems) and distributed system design. Proficiency in at least one modern programming language (e.g., Python, Go, Java, Kotlin, C#) and a "polyglot mindset" to adapt to new stacks quickly. A strong interest in how ML models are built and a passion for solving the engineering challenges of deploying them in the real world. Proficiency in leveraging AI-powered tools (e.g., Claude Code, Codex, Cursor) to accelerate productivity. 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. Preferred Qualifications Experience with the full ML lifecycle, from model training to production deployment. Experience in building AI-based recommendation system. Experience in developing scalable backend servers (handling millions of users). Experience in developing and serving ML-driven services using frameworks such as vLLM, Triton, Ray Serve, or Seldon. Experience with big data or stream processing frameworks (e.g., Spark, Flink, Kafka) for building robust ML data pipelines. Experience in collaborating with cross-functional teams and diverse organizations. Fluent in English (Sophisticated professional interactions with native-level precision and an understanding of cultural nuances.)

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. The Team: The Tinder ML team drives impact across nearly every core domain of the product — Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem. ML at Tinder is organized into three groups with distinct roles: Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training, serving, and feature management Machine Learning Software Engineers who bridge the gap between research and production by delivering machine learning models into real-world product experiences at scale About the Role: We are looking for a Machine Learning Engineer II to help build and ship machine learning systems that improve product experience and drive measurable business impact. This role is ideal for an engineer with a strong foundation in machine learning and software engineering who is excited to work on real-world problems, partner cross-functionally, and grow quickly in a high-impact environment. This is an individual contributor role focused on modeling and algorithmic innovation. You will work closely with product, engineering, data, and platform partners to translate product opportunities into machine learning solutions, run experiments, and help bring models from development into production. The team’s work directly translates into measurable business outcomes, and many of its models are embedded in core Tinder user flows at scale. Where You'll Work: This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, California. In this role, you will: Translate product and business problems into clear machine learning problems with measurable success criteria Build, train, evaluate, and improve production machine learning models Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production Design and analyze offline evaluations and online experiments to understand model impact Contribute to feature engineering, data preparation, training pipelines, and model monitoring Write clean, maintainable, production-quality code and participate in design and code reviews Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. The Role We’re looking for a Founding Full Stack Engineer, AI Incubation to be the technical owner for this initiative. This is a hands-on, high-ownership role for a Staff/Principal-level engineering leader who can build quickly, make strong technical decisions, and help turn an early product thesis into a working product. You’ll work directly on the core experiences users rely on to discover, evaluate, and build meaningful connections. You’ll identify where today’s dating experiences create friction, ambiguity, or low-quality outcomes, and rethink how AI can help create a more useful, trusted, and personalized experience. You should be excited by zero-to-one product building: taking an ambiguous opportunity, defining the fastest path to learning, building the first version, putting it in front of users, and iterating based on real behavior. How you’ll make an impact Build end-to-end product experiences that span frontend applications, backend services, agent workflows, cloud infrastructure, integrations, and internal tooling. Design AI-powered systems that help the product understand user intent, reason about compatibility, personalize recommendations, and improve through feedback over time. Partner closely with product, design, and research to rapidly translate insights into prototypes, pilots, and production-ready features. Use frontier AI tools and modern engineering workflows to compress the path from idea to working product, while maintaining strong technical judgment around reliability, privacy, and scale. Instrument the product with analytics, event tracking, dashboards, and experimentation tools so the team can evaluate engagement, retention, quality, and product-market signal. Improve the reliability, observability, scalability, and performance of the systems and workflows you build. Define the early system architecture and technical roadmap, and serve as a strategic technical thought partner to Match Group leadership. We could be a match if you: 8+ years of software engineering experience, including experience operating at Staff, Principal, founding engineer, technical lead, or equivalent level. A track record of building and shipping consumer products in fast-moving environments, ideally across zero-to-one, early-stage, or startup contexts. Deep full-stack technical range, with the ability to move independently across product surfaces, backend systems, data architecture, infrastructure, and production operations. Experience with mobile development using React Native, Swift, Kotlin, Flutter, or similar technologies. Experience working with modern AI development workflows and practical experience building AI-powered products used by real users High technical judgment around reliability, privacy, security, observability, maintainability, and scale. Clear communication skills and the ability to serve as a trusted technical partner to product, design, research, engineering, and senior leadership. Strong ownership mindset, with the ability to operate without a large team, detailed specs, or fixed roadmap. High judgment around consumer trust, identity, privacy, and emotionally personal user experiences. Nice to have: Experience in dating, social discovery, consumer social, messaging, marketplaces, creator platforms, gaming, community products, or subscription-based consumer products. Experience building AI-native consumer products or AI-powered features used by real users. Experience with recommendation systems, ranking systems, personalization, matching, marketplace liquidity, network effects, or consumer discovery systems. Prior experience as a founder, founding engineer, early startup employee, or zero-to-one product builder.

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Most consumer apps treat your attention as the product, optimizing every recommendation and notification to keep you scrolling. Hinge made the opposite bet: our mission is to get people off the app, out on a date, and eventually to delete us entirely. That is not a tagline. It is the actual constraint every AI system we build has to solve against. The Director of AI, Dating Outcomes owns the hardest version of that problem. You will lead the AI systems at the core of how Hinge connects people: the recommendation engine, the ranking models, and the generative capabilities that help users put their best selves forward. The goal is not engagement, it is outcomes: fewer ratings per exchange, higher quality matches, and more people finding the person they were looking for. Because Hinge does not sell ads or user data, being exceptional at this is our business model. Reporting to the VP of AI, you will set the long-term AI roadmap for one of the company's most influential product areas and build the teams that bring it to life. The problems sit at the intersection of human behavior, sparse real-world signals, and a two-sided ecosystem of people who are not interchangeable, a different kind of ML challenge than most people in this industry have had the chance to work on. Responsibilities Strategy and Vision: Own the AI strategy for dating recommendations across the full journey, from first candidate to last date. Build the frameworks to reason rigorously through ecosystem tradeoffs where a win for one cohort reshapes another, and set a vision ambitious enough that the org wants to chase it. Cross-Functional and Industry Leadership: Partner closely with other product groups to integrate AI capabilities across the product and shape roadmap decisions you do not own. Communicate honestly with executive leadership on strategy, tradeoffs, and progress. Own budget and investment strategy for the area, and represent Hinge externally through writing, talks, and community. Technical Leadership: Steer real-time, personalized recommendation systems that reduce ratings per exchange and drive better matches. Bring Generative AI to dating outcomes in ways that are differentiated, authentic, and grounded in how people actually connect. Responsible AI: Hold the bar on scalability, resilience, and rigorous evaluation for both performance and bias. Team and Organizational Development: Structure and scale the team for both deep execution and long-horizon bets. Develop emerging leaders, invest in succession across IC and leadership tracks, and build a culture where rigor, inclusion, and performance reinforce one another. Execution Excellence: Drive predictable delivery, balancing urgent risk response with long-term platform investments. Establish scalable operating rhythms for planning, technical reviews, and incident learnings. What We're Looking For 10 or more years across data science, applied ML, and software engineering, with 6 or more years in AI or engineering leadership defining strategy, not just managing execution. A track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences. Demonstrated success scaling ML or AI teams and developing technical leaders who carry the work over years, not a single launch. Deep technical fluency across ML, RL, Generative AI, and MLOps, with the communication skills and executive presence to represent AI strategy at the highe

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. Match Group is seeking a Principal AI Learning & Enablement Partner to accelerate how AI is adopted and applied across the organization. This role operates at the intersection of learning, AI, and business operations – connecting brand-level innovation with scaled enablement across Match Group. You will partner closely with AI Brand Leads, business leaders, and functional teams to translate emerging AI capabilities into practical, role-specific applications that drive meaningful behavior change. This role plays a critical part in ensuring employees move beyond basic usage to more advanced, integrated applications of AI in their day-to-day work. This is not a traditional L&D role. It is a cross-brand enablement role focused on surfacing what’s working, scaling best practices, and accelerating adoption across a decentralized ecosystem. This role may travel to key hub cities including Dallas, LA, New York, Vancouver, and Paris; however will be based our of LA, Palo Alto, or San Francisco office and we work in office 3 days/week. What You'll Do Shape the AI Enablement Strategy This role is built for someone who wants to actively shape how AI takes hold at Match Group — not just execute on requests, but bring real perspective. You'll help translate business priorities into a phased, practical roadmap and be a genuine thought partner in how the company moves forward. Develop and maintain a company-wide AI learning roadmap, identifying where AI can create the most meaningful business impact across functions Serve as a strategic advisor to business leaders, translating organizational goals into targeted AI priorities and phased adoption plans Synthesize signals from across the business (adoption trends, emerging tools, team needs) to inform and evolve the enablement approach Influence how Match Group thinks about AI as a competitive capability — not just a productivity tool — and help shape the long-term vision for an AI-native workforce Partner with Business Leaders to Drive Impact Build trusted relationships with senior leaders and functional teams to understand their highest-priority challenges and co-create AI solutions Lead discovery and scoping conversations that translate business problems into concrete AI use cases with measurable outcomes Act as the connective tissue between technical capabilities and business needs — bridging what's possible with what matters Drive alignment across stakeholders on adoption priorities, sequencing, and success metrics Build & Activate AI Capability Across the Organization Own the middle layer of AI enablement — the space between introductory training and deep technical work — where real behavior change and workflow integration happen Design and deliver hands-on learning experiences grounded in real business scenarios, moving employees from basic usage to advanced, integrated application Build scalable resources — playbooks, prompt libraries, workflows, toolkits — that embed AI into how people actually work Close adoption gaps by identifying underutilizing teams and designing targeted enablement approaches, especially for non-technical employees and late adopters Partner with and coordinate internal SMEs and external vendors to deliver high-quality, role-specific training— this role enables teams to execute, it doesn’t deliver everything itself Track adoption and impact: usage, behavior change, and efficiency gains Embed

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Hinge is seeking a versatile Senior Data Scientist to work on product development initiatives on our Matching team. From assessing opportunities and experiments through analytical techniques to modeling user experience with machine learning, you as data scientists at Hinge will inform and evaluate product feature development and strategy. In turn, your work will deeply influence how millions of Hinge members use the app and connect with each other. Responsibilities Collaborate with a cross-functional team of product managers, researchers, designers and engineers to develop and iterate on product features and strategy Translate product objectives to data science problems and vice versa Apply statistical inference to draw rigorous conclusions from data, especially through designing and analyzing A/B tests and working on strategic insights that drive product strategy Use a combination of exploratory analysis and data mining techniques to generate actionable insights about user experience and user problems Collaborate with AI team to develop, iterate, and test production models (e.g., targeting) What We're Looking For 5+ years of experience as data scientist Experience with A/B testing Strong working knowledge of SQL and Python (R is acceptable but we use Python at Hinge) Strong knowledge of statistics and machine learning Superb communication skills Ability to balance rigor with practicality Deep sense of intellectual curiosity Excellent collaboration skills and a track record of cross-functional influence

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. The Role The Principal, Applied AI Enablement role sits at the intersection of legal practice and applied AI — closer to an in-house R&D function than a traditional legal team role. This is a role for a tech-forward, innovative, engineer or product builder. You will identify where AI can do legal work that humans do today — or work humans simply cannot do at scale — prototype and ship AI tools and agents to handle it, and iterate with lawyers in the loop until those tools become a real part of how the function operates. This role is about applying AI to the substance of legal work: the research, analysis, judgment calls, contract review, and regulatory tracking that lawyers do every day. This is not a role about optimizing existing processes or rolling out off-the-shelf legal tech. It is a role about building things that didn't exist before — prototyping quickly, evaluating rigorously, and shipping the ones that work. The role sits within Tinder Legal. You'll work with Tinder Legal leadership and team as your primary domain experts and end users, and partner with engineering, product, and other technical teams where deeper infrastructure is needed. Where capabilities you build prove valuable, there is potential to extend them more broadly — first to other Match Group Legal teams, and ultimately across the portfolio. What You'll Do Identify the highest-leverage AI opportunities Spend time inside Tinder Legal and relevant Match Group Legal teams to understand how legal work happens today — product counseling, marketing reviews, contract review and negotiation, and regulatory compliance — with a focus on where AI can do meaningful legal work, not just where workflow improvements would help. Develop a clear point of view on what AI is genuinely capable of in a legal context today, what's just around the corner, and where human lawyers should remain firmly in the loop. Build the case for which opportunities are worth investing in and which aren't, based on impact, feasibility, and legal risk. Build, prototype, and ship AI tools and agents Design and build AI-powered tools — assistants, agents, retrieval systems, evaluators — that handle real legal work end-to-end or in close collaboration with legal team members. Configure and orchestrate these tools across the data and systems they need to be useful (document repositories, knowledge bases, relevant internal sources), working within IT, Security, and Legal Ops-governed infrastructure — not around it. Treat shipping as the starting line: measure quality against human baselines, debug edge cases, tune prompts and pipelines, and iterate as the underlying models and the business evolve. Train, launch, and iterate with the Tinder legal team Work directly with the legal team and end users to onboard them to new tools and workflows, gather feedback, and make those tools genuinely useful — not just technically functional. Develop the documentation, examples, and attorney-facing training that helps legal team members understand both how to use AI tools and where their judgment must remain the decision-maker. These resources focus on substantive AI capability — how to use AI to do legal work better — and are distinct from Legal Ops resources on legal department systems and workflows. Build evaluation harnesses and quality metrics — accuracy against human baselines, hallucination rates, coverage, latency — and use them

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Joining Hinge’s AI Platform Core team offers a unique opportunity to help define and contribute to a growing ML platform with the goal of accelerating the development, deployment, and operations of AI enabled features on Hinge. If you are excited by the idea of working on challenges ranging from maturing and scaling our training and serving frameworks to enable millisecond real-time predictions and models that continually learn at scale, providing automatic model performance monitoring & debug frameworks, and building Platforms for architecting and evaluating generative AI & Agentic systems, then read on! With a mission to democratize AI for Hinge, ensuring it remains easily accessible, robust, scalable, cost-efficient, and trusted. You'll collaborate across our product and platform teams, as well as with external stakeholders like data science and backend engineering. You will meet our internal customers where they are and optimize towards their needs by delivering value incrementally and coupled to their problems. Being part of a small yet impactful team means having a broad scope of responsibility. As the role of ML at Hinge is only becoming more impactful, this role provides you with a chance to grow in impact and scope across the company. This is an exciting opportunity to own and help define the future of machine learning within a growing and maturing team! Responsibilities Own or contribute to technical designs for the feature platform, training platform, serving platform, AI observability platform and GenAI Platform and underlying operational infrastructure to enable Product impact. Develop, maintain, and enhance frameworks for AI/ML model development and deployment while establishing and driving best practices in MLOps / GenAI engineering. Design, advocate, and implement for usability, reliability, scalability, operational excellence, and cost management while delivering incrementally. Collaborate closely with ML Engineers, Data Scientists, Data Engineers and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process. Mentor and educate ML Engineers, Backend Engineers and Product Managers on current and up and coming tools and technologies for ML operations & GenAI product development through presentations and documentation. Help design and architect an AI platform that adheres to the principles of responsible AI (Authenticity, Transparency, Equity) and builds privacy compliance into the platform. Lead build vs buy discussions on technologies that underpin the Platforms we serve. Participate in on-call and Incident Management processes. What We're Looking For 4+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems. 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes 1+ year of experience leading projects with at least 1 other team member through completion. 1+ year of experience for Senior designing and developing online and production grade ML systems. A degree in computer science, engineering, or a related field. Strong programming skills: Proficiency in languages like Python, Go, or Java. System design & architecture : Ability to design scalable and efficient ML systems. Cloud platform proficiency : The ability to utilize cloud environments such as GCP, AWS, or Azure. ML knowledge : A basic understandi

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role: We are hiring a Staff Machine Learning Engineer to drive the design, development, and deployment of machine learning systems that power our core products in User Growth and Monetization. You will work on building deeply personalized user experiences that drive business growth and meet individual needs and preferences: by targeting the right users with the right products at the right time, we empower product teams to drive meaningful growth in revenue and user engagement while enhancing user satisfaction. The Growth Product Group is responsible for the development of paid features to accelerate dating outcomes, driving engagement throughout a user’s lifecycle and making Hinge the dating destination for all communities. You can expect to work on a broad range of problems, from identifying how to send the right message to the right user at the right time to optimizing the efficacy of our paid offerings. This is a fast growing team, and you will help define the vision and strategy that drives meaningful growth and accelerates machine learning at Hinge. Responsibilities: Lead the end-to-end development of production-grade ML systems such as user targeting models that will help drive engagement, improve dating outcomes and/or improve user adoption of and engagement with paid features Define and own the technical roadmap for ML within your product area and align with company-wide priorities Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space. Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale. Ensure the ethical and responsible use of ML/AI and compliance with privacy regulations to protect user data Communicate effectively to audiences of various technical and non-technical backgrounds What We're Looking For: Strong programming skills: Proficiency in Python and ML libraries such as PyTorch Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning machine learning for user facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus. System design & architecture: Strong background in setting up and optimizing ML infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for ML (e.g., model versioning, automated testing). Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow, or Weights & Biases is a plus. Data engineering knowledge: Skills in handling and managing large datasets including data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow. Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.. Software leadership skills: A track record of leading projec

-Legal Entity: Hyperconnect -Brand: Match Group AI Match Group AI 소개 Match Group AI는 온라인 데이팅의 핵심 문제를 AI로 해결하는 중앙 기술 조직입니다. 프로필, 매칭, Trust & Safety 등 사용자 경험의 핵심 영역에 집중해 과제를 발굴합니다. 이를 최신 AI 기술과 데이터 기반의 접근을 통해 해결하여 사용자 경험을 혁신합니다. 나아가 Tinder, Hinge 등 Match Group 내 글로벌 데이팅 브랜드와의 협업을 바탕으로 그룹 공통 기술 기반을 확장합니다. 팀에서 진행 중인 프로젝트를 더 자세히 알고 싶으시다면, 다음 게시글을 확인해보세요. 👉 "Match Group AI 팀을 소개합니다." Match Group AI ML Team 소개 ML Team은 Match Group의 다양한 서비스에 AI/ML 기술을 적용하는 ML Engineer로 구성된 팀입니다. 팀의 업무는 실제 제품이 만들어지고 운영되는 과정에서 발생하는 문제를 발굴하고 정의하는 것에서 시작됩니다. 문제 해결에 가장 적합한 SotA(State-of-the-Art) 모델을 개발하거나 재현하고, 완성된 모델을 모바일과 서버 환경에 안정적이고 효율적으로 배포합니다. 이후에도 지속적인 모니터링과 개선을 통해 서비스의 AI Flywheel을 구축해 나갑니다. 이 과정에서 백엔드/프론트엔드/DevOps 엔지니어, 데이터 분석가, PM 등 다양한 전문 조직과 긴밀히 협업하며 실제 사용자에게 임팩트를 주는 AI 경험을 만들어갑니다. 일하는 모습에 대한 조금 더 자세한 이야기는 다음의 내용을 참고하시면 좋습니다. - [How AI Lab Works] Head of AI - Shurain 인터뷰 - AI in Social Discovery(Blending Research and Production) 업무 성과 중 일부는 논문 또는 오픈소스 코드로 외부에 공개하기도 합니다. 제품에 사용하기 위한 목적으로 ML 모델을 만들다 보면, 기존 연구로는 부족한 경우가 많습니다. 부족한 부분을 채우기 위해 진행된 연구의 결과물을 프로젝트 참여자들이 모두 함께 협업하여 연구의 의미 있는 부분을 정돈하고, 가능하다면 코드와 함께 공개합니다. 그 결과, 지금까지 아래 성과를 포함해 약 20여 건의 대외적 연구 성과를 거둘 수 있었습니다. - 2024년 CUPID: 1:1 소셜 디스커버리 플랫폼을 위한 실시간 세션 기반 상호 추천 시스템 ICDM Workshop 발표 - 2023년 TiDAL: 효율적인 학습 과정의 모델 행동에 기반한 액티브 러닝 기법 ICCV 2023 게재 - 2023년 모더레이션 환경에서 여러 분류 기준을 동시에 만족하기 위한 문턱값을 잡는 연구 WSDM 2023 게재 - 2022년 대화 생성에서의 의미적 다양성을 높이는 연구 EMNLP 2022 게재 - 2022년 레이블 노이즈가 심한 환경에서 효과적으로 학습하는 방법 ECCV 2022 게재 - 2022년 타깃 캐릭터의 몇가지 발화만을 이용하여 타깃 캐릭터를 모방하는 챗봇연구 NAACL 2022 게재 - 2022년 대화 생성 모델에서 예시를 활용하여 성능을 높이는 연구 ACL 2022 Workshop 발표 - 2022년 모바일 환경에서 오디오 분류를 위한 distillation 기술 연구 ICASSP 게재 ML 연구가 잘 진행되기 위해서는 딥러닝 학습을 위한 인프라도 잘 갖추어져야합니다. ML engineer들이 충분히 모델 개발 및 실험을 진행할 수 있도록 자체 딥러닝 클러스터(총 160대 A100 GPU, 40대 H100 GPU)를 보유하고 있습니다. 추가적으로, 데이터 수집, 전처리를 포함한 자체적인 데이터 파이프라인을 클라우드 서비스를 활용하여 구축 및 운영하고 있습니다. 또, ML 모델의 제품화를 도와주실 다양한 software engineer(백엔드/프론트엔드/DevOps/MLSE)분들과 함께 일하고 있습니다. 담당 업무 Match Group AI의 ML Engineer는 최신 AI/ML 기술을 연구하고 적용하는 과학자이자, 실제 서비스 환경에 맞게 모델 및 시스템을 설계하고 최적화하는 엔지니어입니다. 조직에서는 다음과 같은 다양한 업무들을 담당하고 있으며, 이들 중 적어도 한 가지 업무에 대해서 능숙하게 수행하실 수 있는 분을 찾고 있습니다. - Match Group에서 서비스 중인 제품들에서 발생하는 다양한 비즈니스 문제를 해결합니다. 비즈니스 문제의 배경, 목표, 제약사항 등을 파악하여 올바른 AI/ML 문제로 재정의하고, 이를 해결하기 위한 최선의 방법론을 찾아 문제를 해결하는 과정을 수행합니다. - Match Group의 새로운 제품 및 기능 개발에 참여합니다. 아이디어 구상부터 프로토타이핑, 실제 사용자에게 도달하기까지의 과정에서 AI/ML 기술들을 활용하여 제품 및 기능을 빠르고 올바르게 구현하는 과정을 수행합니다. - 대규모 언어 모델(large language models) 및 멀티모달 모델(multimodal models) 선행연구 및 개발을 담당합니다. Match Group의 데이터와 최신 기술들을 활용하여 데이팅 도메인에 특화된 모델을 만들고, 제품의 다양한 영역에 적용시킬 수 있는 방법을 찾아가는 과정을 수행합니다. 위 업무들을 수행하기 위해 Sr. ML Engineer에게는 탄탄한 AI/ML 기초 지식과 빠른 최신 기술 습득을 기반으로 팀의 기술적 의사결정을 이끌어 갈 수 있는 역량을 기대합니다. 이러한 역량을 기반으로 주로 다음과 같은 기술적인 주제들을 주도하게 됩니다. - 텍스트, 이미지, 이벤트 로그 등을 포함하여 다양한 modality를 가진 데이터를 올바르게 다루는 방법 및 수집된 데이터의 편향 및 노이즈를 올바르게 해소하는 방법 - 제품의 목표와 정렬된 평가 지표 정의 및 목표 달성을 위한 최적의 모델 학습 방법 - 엔지니어링 제약사항 및 인프라 환경을 고려한 온디바이스 및 대규모 모델 추론 최적화 방법 Requirements AI/ML 도메인 전반에 대한 이해와 적어도 한 개 이상의 특정 도메인에 대한 깊이 있는 지식을 갖추고, 관련 분야 박사 학위 혹은 5년 이상 의 산업 프로젝트 경험, 혹은 그에 준하는 역량을 보유하고 계신 분 모호한 비즈니스 혹은 기술적인 문제가 주어졌을 때 이를 올바른 AI/ML 문제로 정의하고, 합리적인 기술적 의사결정을 바탕으로 문제를 해결하실 수 있는 분 PyTorch, Tensorflow, JAX 등의 오픈소스 프레임워크 및 전반적인 파이썬 개발을 빠르게 수행하고, 협업 및 유지보수를 위해 높은 품질의 코드를 관리하실 수 있는 분 안정성있는 ML 학습 파이프라인 및 배포 프로세스를 설계하실 수 있는 분 끊임없이 새로운 기술을 탐구하고 공유하며 팀이 높은 수준의 AI/ML 기술적 경쟁력을 갖추고 유지하도록 도와주실 수 있는 분 두 명 이상의 ML Engineer가 참여하는 AI 프로젝트에서 기술적인 의사결정을 이끌어갈 수 있으신 분 여러 직군의 이해관계자와 협업할 수 있는 뛰어난 커뮤니케이션 능력을 갖추신 분 한국어로 원활한 의사소통이 가능하고, 영어로 기본적인 의사소통이 가능하신 분 Preferred Qualifications AI/ML 관련 탑티어 학회 및 저널(NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, KDD, …) 게재 실적 혹은 관련 대회 수상 실적이 있으신 분 LLM 혹은 비슷한 규모의 모델을 대규모로 학습할 때 필요한 전체 과정을 이끌며 성과를 만들어낸 경험이 있으신 분 대규모 소프트웨어 개발 경험 및 아키텍쳐/시스템 디자인에 필요한 엔지니어링 역량을 갖추고 계신 분 A/B 테스트 실험 기획 및 타겟 KPI 지표를 올바르게 정의하고, SQL기반 데이터 분석을 전문성 있게 수행하실 수 있으신 분 인과관계 분석 및 기타 통계 기법을 사용하여 데이터에서 의미 있는 통찰력을 발굴하고 의사결정에 올바르게 적용하실 수 있는 분 엔지니어링 팀을 리드해본 경험이 있으신 분 PO/PM 혹은 그에 준하는 경험이 있으신 분 영어로 실시간 비즈니스 의사소통을 능숙하게 수행하실 수 있는 분 Hiring Process 고용 형태: 정규직 채용 절차: 서

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Most consumer apps treat your attention as the product, optimizing every recommendation and notification to keep you scrolling. Hinge made the opposite bet: our mission is to get people off the app, out on a date, and eventually to delete us entirely. That is not a tagline. It is the actual constraint every AI system we build has to solve against. The Director of AI, Dating Outcomes owns the hardest version of that problem. You will lead the AI systems at the core of how Hinge connects people: the recommendation engine, the ranking models, and the generative capabilities that help users put their best selves forward. The goal is not engagement, it is outcomes: fewer ratings per exchange, higher quality matches, and more people finding the person they were looking for. Because Hinge does not sell ads or user data, being exceptional at this is our business model. Reporting to the VP of AI, you will set the long-term AI roadmap for one of the company's most influential product areas and build the teams that bring it to life. The problems sit at the intersection of human behavior, sparse real-world signals, and a two-sided ecosystem of people who are not interchangeable, a different kind of ML challenge than most people in this industry have had the chance to work on. Responsibilities Strategy and Vision: Own the AI strategy for dating recommendations across the full journey, from first candidate to last date. Build the frameworks to reason rigorously through ecosystem tradeoffs where a win for one cohort reshapes another, and set a vision ambitious enough that the org wants to chase it. Cross-Functional and Industry Leadership: Partner closely with other product groups to integrate AI capabilities across the product and shape roadmap decisions you do not own. Communicate honestly with executive leadership on strategy, tradeoffs, and progress. Own budget and investment strategy for the area, and represent Hinge externally through writing, talks, and community. Technical Leadership: Steer real-time, personalized recommendation systems that reduce ratings per exchange and drive better matches. Bring Generative AI to dating outcomes in ways that are differentiated, authentic, and grounded in how people actually connect. Responsible AI: Hold the bar on scalability, resilience, and rigorous evaluation for both performance and bias. Team and Organizational Development: Structure and scale the team for both deep execution and long-horizon bets. Develop emerging leaders, invest in succession across IC and leadership tracks, and build a culture where rigor, inclusion, and performance reinforce one another. Execution Excellence: Drive predictable delivery, balancing urgent risk response with long-term platform investments. Establish scalable operating rhythms for planning, technical reviews, and incident learnings. What We're Looking For 10 or more years across data science, applied ML, and software engineering, with 6 or more years in AI or engineering leadership defining strategy, not just managing execution. A track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences. Demonstrated success scaling ML or AI teams and developing technical leaders who carry the work over years, not a single launch. Deep technical fluency across ML, RL, Generative AI, and MLOps, with the communication skills and executive presence to represent AI strategy at the highe

Know where you belong! Match Group is a leading provider of dating products across the globe. Our portfolio includes Tinder, Hinge, Match, Meetic, PlentyOfFish, OkCupid, The League, HER, and others, each designed to spark meaningful connections for singles worldwide. Creating a sense of belonging doesn’t stop at our products - it’s the foundation of every team we hire. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days/week. The Role The Principal, Applied AI Enablement role sits at the intersection of legal practice and applied AI — closer to an in-house R&D function than a traditional legal team role. This is a role for a tech-forward, innovative, engineer or product builder. You will identify where AI can do legal work that humans do today — or work humans simply cannot do at scale — prototype and ship AI tools and agents to handle it, and iterate with lawyers in the loop until those tools become a real part of how the function operates. This role is about applying AI to the substance of legal work: the research, analysis, judgment calls, contract review, and regulatory tracking that lawyers do every day. This is not a role about optimizing existing processes or rolling out off-the-shelf legal tech. It is a role about building things that didn't exist before — prototyping quickly, evaluating rigorously, and shipping the ones that work. The role sits within Tinder Legal. You'll work with Tinder Legal leadership and team as your primary domain experts and end users, and partner with engineering, product, and other technical teams where deeper infrastructure is needed. Where capabilities you build prove valuable, there is potential to extend them more broadly — first to other Match Group Legal teams, and ultimately across the portfolio. What You'll Do Identify the highest-leverage AI opportunities Spend time inside Tinder Legal and relevant Match Group Legal teams to understand how legal work happens today — product counseling, marketing reviews, contract review and negotiation, and regulatory compliance — with a focus on where AI can do meaningful legal work, not just where workflow improvements would help. Develop a clear point of view on what AI is genuinely capable of in a legal context today, what's just around the corner, and where human lawyers should remain firmly in the loop. Build the case for which opportunities are worth investing in and which aren't, based on impact, feasibility, and legal risk. Build, prototype, and ship AI tools and agents Design and build AI-powered tools — assistants, agents, retrieval systems, evaluators — that handle real legal work end-to-end or in close collaboration with legal team members. Configure and orchestrate these tools across the data and systems they need to be useful (document repositories, knowledge bases, relevant internal sources), working within IT, Security, and Legal Ops-governed infrastructure — not around it. Treat shipping as the starting line: measure quality against human baselines, debug edge cases, tune prompts and pipelines, and iterate as the underlying models and the business evolve. Train, launch, and iterate with the Tinder legal team Work directly with the legal team and end users to onboard them to new tools and workflows, gather feedback, and make those tools genuinely useful — not just technically functional. Develop the documentation, examples, and attorney-facing training that helps legal team members understand both how to use AI tools and where their judgment must remain the decision-maker. These resources focus on substantive AI capability — how to use AI to do legal work better — and are distinct from Legal Ops resources on legal department systems and workflows. Build evaluation harnesses and quality metrics — accuracy against human baselines, hallucination rates, coverage, latency — and use them

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Hinge is seeking a versatile Senior Data Scientist to work on product development initiatives on our Matching team. From assessing opportunities and experiments through analytical techniques to modeling user experience with machine learning, you as data scientists at Hinge will inform and evaluate product feature development and strategy. In turn, your work will deeply influence how millions of Hinge members use the app and connect with each other. Responsibilities Collaborate with a cross-functional team of product managers, researchers, designers and engineers to develop and iterate on product features and strategy Translate product objectives to data science problems and vice versa Apply statistical inference to draw rigorous conclusions from data, especially through designing and analyzing A/B tests and working on strategic insights that drive product strategy Use a combination of exploratory analysis and data mining techniques to generate actionable insights about user experience and user problems Collaborate with AI team to develop, iterate, and test production models (e.g., targeting) What We're Looking For 5+ years of experience as data scientist Experience with A/B testing Strong working knowledge of SQL and Python (R is acceptable but we use Python at Hinge) Strong knowledge of statistics and machine learning Superb communication skills Ability to balance rigor with practicality Deep sense of intellectual curiosity Excellent collaboration skills and a track record of cross-functional influence

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Most consumer apps treat your attention as the product, optimizing every recommendation and notification to keep you scrolling. Hinge made the opposite bet: our mission is to get people off the app, out on a date, and eventually to delete us entirely. That is not a tagline. It is the actual constraint every AI system we build has to solve against. The Director of AI, Dating Outcomes owns the hardest version of that problem. You will lead the AI systems at the core of how Hinge connects people: the recommendation engine, the ranking models, and the generative capabilities that help users put their best selves forward. The goal is not engagement, it is outcomes: fewer ratings per exchange, higher quality matches, and more people finding the person they were looking for. Because Hinge does not sell ads or user data, being exceptional at this is our business model. Reporting to the VP of AI, you will set the long-term AI roadmap for one of the company's most influential product areas and build the teams that bring it to life. The problems sit at the intersection of human behavior, sparse real-world signals, and a two-sided ecosystem of people who are not interchangeable, a different kind of ML challenge than most people in this industry have had the chance to work on. As a member of our team, you’ll enjoy: 401(k) Matching: We match 100% of the first 10% of pre-tax 401(k) contributions you make, up to a maximum of $10,000 per year. Professional Growth: Get an annual Learning & Development stipend once you’ve been with us for three months. You also get free access to Udemy, an online learning and teaching marketplace with over 6000 courses, starting your first day. Parental Leave & Planning: When you become a new parent, you’re eligible for 100% paid parental leave (20 paid weeks for both birth and non-birth parents.) Fertility Support: You’ll get easy access to fertility care through Carrot, from basic treatments to fertility preservation. We also provide a stipend towards fertility preservation. You and your spouse/domestic partner are both eligible. Date Stipend: All Hinge employees receive a $100 monthly stipend for epic dates– Romantic or otherwise. Hinge Premium is also free for employees and their loved ones. ERGs: We have eight Employee Resource Groups (ERGs)—Asian, Unapologetic, Disability, LGBTQIA+, Raices, Women/Nonbinary, Parents —that hold regular meetings, host events, and provide dedicated support to the organization & its community. At Hinge, our core values are… Authenticity: We share, never hide, our words, actions and intentions. Courage: We embrace lofty goals and tough challenges. Empathy: We deeply consider the perspective of others. Diversity inspires innovation Hinge is an equal-opportunity employer. We value diversity at our company and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We believe success is created by a diverse workforce of individuals with different ideas, strengths, interests, and cultural backgrounds. If you require reasonable accommodation to complete a job application, pre-employment testing, or a job interview or to otherwise participate in the hiring process, please let your Talent Acquisition partner know. Hinge Responsibilities Strategy and Vision: Own the AI strategy for dating recommendations across the full journey, from first candidate to last date. Build the frameworks to reason rigorously through ecosystem tradeoffs where a win for one cohort reshapes another, and set a vision ambitious enough that the org wants to chase it. Cross-Functional and Industry Leadership: Partner closely with other product groups to integrate AI capabilities across the product and shape roadmap decisions you do not own. Communicate honestly with executive leadership on strategy, tradeoffs, and progress. Own budget and investment strategy for the area, and represent Hinge externally through writing, talks, and community. Technical Leadership: Steer real-time, personalized recommendation systems that reduce ratings per exchange and drive better matches. Bring Generative AI to dating outcomes in ways that are differentiated, authentic, and grounded in how people actually connect. Responsible AI: Hold the bar on scalability, resilience, and rigorous evaluation for both performance and bias. Team and Organizational Development: Structure and scale the team for both deep execution and long-horizon bets. Develop emerging leaders, invest in succession across IC and leadership tracks, and build a culture where rigor, inclusion, and performance reinforce one another. Execution Excellence: Drive predictable delivery, balancing urgent risk response with long-term platform investments. Establish scalable operating rhythms for planning, technical reviews, and incident learnings. What We're Looking For 10 or more years across data science, applied ML, and software engineering, with 6 or more years in AI or engineering leadership defining strategy, not just managing execution. A track record of leading real-time, personalized recommendation systems in production, including the relevance, fairness, and scale tradeoffs that carry real consequences. Demonstrated success scaling ML or AI teams and developing technical leaders who carry the work over years, not a single launch. Deep technical fluency across ML, RL, Generative AI, and MLOps, with the communication skills and executive presence to represent AI strategy at the highest levels of the organization. A degree in Computer Science, Machine Learning, or a related quantitative field. A PhD is a plus, not a requirement. What Will Set You Apart Experience building AI systems in a two-sided or constrained marketplace, where optimizing for one group shifts outcomes for another and the right answer is rarely obvious. A history of deploying Generative AI thoughtfully in user-facing experiences, not as a trend to follow. External presence through publishing, speaking, or community contribution that reflects genuine intellectual engagement with the field. Prior work in a domain where user outcomes are high-stakes and hard to measure, whether health, education, finance, or something comparably consequential.

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Join Hinge as a Senior Machine Learning Engineer, where you'll lead the application of AI and machine learning to effectively mitigate the impact of bad actors, remove policy violating content, and ensure user safety on the platform. Working closely with an expanding Trust & Safety team, you will collaborate with product managers, data scientists, engineers, and analysts to develop impactful AI/ML solutions. In this high-impact position within a small, dynamic team, you'll have the opportunity to play a foundational role in shaping how Hinge utilizes AI and machine learning in various contexts. Your expertise will be instrumental in creating a safer and more meaningful user experience on their journey to find an intimate connection. Responsibilities Develop, deploy, and maintain end-to-end machine learning models to identify and mitigate bad actors, remove policy-violating content, and ensure user safety. Design and implement scalable systems (e.g., using Spark, Kubernetes) to preprocess data, run inference, and manage post-processing pipelines. Define standardized performance metrics, testing protocols, and evaluation processes to measure the effectiveness, identify and mitigate potential risks, and ensure fairness of AI solutions. Ensure ongoing assessment and refinement of AI solutions, incorporating user feedback, business impact, and emerging ethical considerations. Collaborate closely with Data Scientists, Data Engineers, Product Managers, Backend Engineers, and the AI Platform Team to ensure a comprehensive and coordinated approach to user safety. Stay abreast of new trends and research in AI/ML that can be applied to Trust & Safety initiatives to improve detection, user safety, and stay ahead of potential threats. What We're Looking For Strong programming skills: Proficiency in languages like Python, Java or C++ and SQL, proficiency in at least one ML stack (e.g., PyTorch), and strong understanding of data pipelines Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning real-time machine learning models for user facing products. Experience with applying expertise to fraud detection, content moderation, or related fields is a plus . System design & architecture: Experience training and deploying large scale ML models. Good understanding of distributed computing for learning and inference. Evaluation frameworks for LLMs: Experience designing robust testing protocols to ensure effectiveness, fairness, and safety of AI-driven features. Cloud and data platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with solutions like Databricks, Ray, or KubeFlow is a plus. Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow. Strategic technical leadership skills: Demonstrated track record of guiding teams through complex ML projects in alignment with product and business objectives. Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds. Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation. Prior Experience 4+ years of experience

Our Mission As humans, there are few things more exciting than meeting someone new. At Tinder, we’re inspired by the challenge of keeping the magic of human connection alive. With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day, and a presence in 190+ countries, our reach is expansive—and rapidly growing. We work together to solve complex problems. Behind the simplicity of every match, we think deeply about human relationships, behavioral science, network economics, AI and ML, online and real-world safety, cultural nuances, loneliness, love, sex, and more. Our Values Take the Lead: We don't ghost our work or each other. Just as users don't leave their matches hanging, we don't let each other down. Move Fast: We have a bias for action and urgency. Something that could be done tomorrow would be better if done today. Better Together: We keep connection at the heart of dating and at the heart of how we work. Just as our users are better when they connect with others, so are we when we collaborate. Real Talk: We say the hard thing the human way. Just as we ask our users to behave with kindness and candor in our community, we expect Team Tinder to do the same. Safety First: We act with integrity, transparency, and consistency so people feel safe—whether they're swiping, matching, or working alongside us. Spark Fun: We have fun to unlock creativity, fuel innovation, and help us build better experiences for daters. The Team or Role: The Tinder ML team drives impact across nearly every core domain of the product — Recommendations, Trust & Safety, Profile, Chat, Growth, and Revenue optimization. Our mission is to apply machine learning to enhance user experiences, foster trust, and accelerate business growth across Tinder’s ecosystem. ML at Tinder is organized into three groups with distinct roles: Machine Learning Engineers who focus on modeling and algorithmic innovation (this role) Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training, serving, and feature management Machine Learning Software Engineers who bridge the gap between research and production by delivering machine learning models into real-world product experiences at scale About the Role We are looking for a Machine Learning Engineer II to help build and ship machine learning systems that improve product experience and drive measurable business impact. This role is ideal for an engineer with a strong foundation in machine learning and software engineering who is excited to work on real-world problems, partner cross-functionally, and grow quickly in a high-impact environment. This is an individual contributor role focused on modeling and algorithmic innovation. You will work closely with product, engineering, data, and platform partners to translate product opportunities into machine learning solutions, run experiments, and help bring models from development into production. The team’s work directly translates into measurable business outcomes, and many of its models are embedded in core Tinder user flows at scale. Where You'll Work: This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, California. In this role, you will: Translate product and business problems into clear machine learning problems with measurable success criteria Build, train, evaluate, and improve production machine learning models Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production Design and analyze offline evaluations and online experiments to understand model impact Contribute to feature engineering, data preparation, training pipelines, and model monitoring Write clean, maintainable, production-quality code and participate in design and code reviews Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners You'll need: BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field 1+ year of industry experience in machine learning, software engineering, data science, or a related field Strong foundation in computer science fundamentals, including data structures, algorithms, and software design Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation Strong communication skills and the ability to collaborate effectively across functions Self-motivated, proactive, and comfortable taking ownership of well-scoped problems Nice to have: Experience with recommendation systems or casual inference Familiarity with big data or stream processing frameworks such as Spark or Flink Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation Exposure to observability and monitoring for ML systems Exposure to LLM-related use cases or applied generative AI projects

-Legal Entity: Hyperconnect -Brand: Match Group AI Match Group AI 소개 Match Group AI는 온라인 데이팅의 핵심 문제를 AI로 해결하는 중앙 기술 조직입니다. 프로필, 매칭, Trust & Safety 등 사용자 경험의 핵심 영역에 집중해 과제를 발굴합니다. 이를 최신 AI 기술과 데이터 기반의 접근을 통해 해결하여 사용자 경험을 혁신합니다. 나아가 Tinder, Hinge 등 Match Group 내 글로벌 데이팅 브랜드와의 협업을 바탕으로 그룹 공통 기술 기반을 확장합니다. 팀에서 진행 중인 프로젝트를 더 자세히 알고 싶으시다면, 다음 게시글을 확인해보세요. 👉 "Match Group AI 팀을 소개합니다." Match Group AI ML Team 소개 ML Team은 Match Group의 다양한 서비스에 AI/ML 기술을 적용하는 ML Engineer로 구성된 팀입니다. 팀의 업무는 실제 제품이 만들어지고 운영되는 과정에서 발생하는 문제를 발굴하고 정의하는 것에서 시작됩니다. 문제 해결에 가장 적합한 SotA(State-of-the-Art) 모델을 개발하거나 재현하고, 완성된 모델을 모바일과 서버 환경에 안정적이고 효율적으로 배포합니다. 이후에도 지속적인 모니터링과 개선을 통해 서비스의 AI Flywheel을 구축해 나갑니다. 이 과정에서 백엔드/프론트엔드/DevOps 엔지니어, 데이터 분석가, PM 등 다양한 전문 조직과 긴밀히 협업하며 실제 사용자에게 임팩트를 주는 AI 경험을 만들어갑니다. 일하는 모습에 대한 조금 더 자세한 이야기는 다음의 내용을 참고하시면 좋습니다. - [How AI Lab Works] Head of AI - Shurain 인터뷰 - AI in Social Discovery(Blending Research and Production) 업무 성과 중 일부는 논문 또는 오픈소스 코드로 외부에 공개하기도 합니다. 제품에 사용하기 위한 목적으로 ML 모델을 만들다 보면, 기존 연구로는 부족한 경우가 많습니다. 부족한 부분을 채우기 위해 진행된 연구의 결과물을 프로젝트 참여자들이 모두 함께 협업하여 연구의 의미 있는 부분을 정돈하고, 가능하다면 코드와 함께 공개합니다. 그 결과, 지금까지 아래 성과를 포함해 약 20여 건의 대외적 연구 성과를 거둘 수 있었습니다. - 2024년 CUPID: 1:1 소셜 디스커버리 플랫폼을 위한 실시간 세션 기반 상호 추천 시스템 ICDM Workshop 발표 - 2023년 TiDAL: 효율적인 학습 과정의 모델 행동에 기반한 액티브 러닝 기법 ICCV 2023 게재 - 2023년 모더레이션 환경에서 여러 분류 기준을 동시에 만족하기 위한 문턱값을 잡는 연구 WSDM 2023 게재 - 2022년 대화 생성에서의 의미적 다양성을 높이는 연구 EMNLP 2022 게재 - 2022년 레이블 노이즈가 심한 환경에서 효과적으로 학습하는 방법 ECCV 2022 게재 - 2022년 타깃 캐릭터의 몇가지 발화만을 이용하여 타깃 캐릭터를 모방하는 챗봇연구 NAACL 2022 게재 - 2022년 대화 생성 모델에서 예시를 활용하여 성능을 높이는 연구 ACL 2022 Workshop 발표 - 2022년 모바일 환경에서 오디오 분류를 위한 distillation 기술 연구 ICASSP 게재 ML 연구가 잘 진행되기 위해서는 딥러닝 학습을 위한 인프라도 잘 갖추어져야합니다. ML engineer들이 충분히 모델 개발 및 실험을 진행할 수 있도록 자체 딥러닝 클러스터(총 160대 A100 GPU, 40대 H100 GPU)를 보유하고 있습니다. 추가적으로, 데이터 수집, 전처리를 포함한 자체적인 데이터 파이프라인을 클라우드 서비스를 활용하여 구축 및 운영하고 있습니다. 또, ML 모델의 제품화를 도와주실 다양한 software engineer(백엔드/프론트엔드/DevOps/MLSE)분들과 함께 일하고 있습니다. 담당 업무 Match Group AI의 ML Engineer는 최신 AI/ML 기술을 연구하고 적용하는 과학자이자, 실제 서비스 환경에 맞게 모델 및 시스템을 설계하고 최적화하는 엔지니어입니다. 조직에서는 다음과 같은 다양한 업무들을 담당하고 있으며, 이들 중 적어도 한 가지 업무에 대해서 능숙하게 수행하실 수 있는 분을 찾고 있습니다. - Match Group에서 서비스 중인 제품들에서 발생하는 다양한 비즈니스 문제를 해결합니다. 비즈니스 문제의 배경, 목표, 제약사항 등을 파악하여 올바른 AI/ML 문제로 재정의하고, 이를 해결하기 위한 최선의 방법론을 찾아 문제를 해결하는 과정을 수행합니다. - Match Group의 새로운 제품 및 기능 개발에 참여합니다. 아이디어 구상부터 프로토타이핑, 실제 사용자에게 도달하기까지의 과정에서 AI/ML 기술들을 활용하여 제품 및 기능을 빠르고 올바르게 구현하는 과정을 수행합니다. - 대규모 언어 모델(large language models) 및 멀티모달 모델(multimodal models) 선행연구 및 개발을 담당합니다. Match Group의 데이터와 최신 기술들을 활용하여 데이팅 도메인에 특화된 모델을 만들고, 제품의 다양한 영역에 적용시킬 수 있는 방법을 찾아가는 과정을 수행합니다. 위 업무들을 수행하기 위해 Sr. ML Engineer에게는 탄탄한 AI/ML 기초 지식과 빠른 최신 기술 습득을 기반으로 팀의 기술적 의사결정을 이끌어 갈 수 있는 역량을 기대합니다. 이러한 역량을 기반으로 주로 다음과 같은 기술적인 주제들을 주도하게 됩니다. - 텍스트, 이미지, 이벤트 로그 등을 포함하여 다양한 modality를 가진 데이터를 올바르게 다루는 방법 및 수집된 데이터의 편향 및 노이즈를 올바르게 해소하는 방법 - 제품의 목표와 정렬된 평가 지표 정의 및 목표 달성을 위한 최적의 모델 학습 방법 - 엔지니어링 제약사항 및 인프라 환경을 고려한 온디바이스 및 대규모 모델 추론 최적화 방법 Requirements AI/ML 도메인 전반에 대한 이해와 적어도 한 개 이상의 특정 도메인에 대한 깊이 있는 지식을 갖추고, 관련 분야 박사 학위 혹은 5년 이상 의 산업 프로젝트 경험, 혹은 그에 준하는 역량을 보유하고 계신 분 모호한 비즈니스 혹은 기술적인 문제가 주어졌을 때 이를 올바른 AI/ML 문제로 정의하고, 합리적인 기술적 의사결정을 바탕으로 문제를 해결하실 수 있는 분 PyTorch, Tensorflow, JAX 등의 오픈소스 프레임워크 및 전반적인 파이썬 개발을 빠르게 수행하고, 협업 및 유지보수를 위해 높은 품질의 코드를 관리하실 수 있는 분 안정성있는 ML 학습 파이프라인 및 배포 프로세스를 설계하실 수 있는 분 끊임없이 새로운 기술을 탐구하고 공유하며 팀이 높은 수준의 AI/ML 기술적 경쟁력을 갖추고 유지하도록 도와주실 수 있는 분 두 명 이상의 ML Engineer가 참여하는 AI 프로젝트에서 기술적인 의사결정을 이끌어갈 수 있으신 분 여러 직군의 이해관계자와 협업할 수 있는 뛰어난 커뮤니케이션 능력을 갖추신 분 한국어로 원활한 의사소통이 가능하고, 영어로 기본적인 의사소통이 가능하신 분 Preferred Qualifications AI/ML 관련 탑티어 학회 및 저널(NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, KDD, …) 게재 실적 혹은 관련 대회 수상 실적이 있으신 분 LLM 혹은 비슷한 규모의 모델을 대규모로 학습할 때 필요한 전체 과정을 이끌며 성과를 만들어낸 경험이 있으신 분 대규모 소프트웨어 개발 경험 및 아키텍쳐/시스템 디자인에 필요한 엔지니어링 역량을 갖추고 계신 분 A/B 테스트 실험 기획 및 타겟 KPI 지표를 올바르게 정의하고, SQL기반 데이터 분석을 전문성 있게 수행하실 수 있으신 분 인과관계 분석 및 기타 통계 기법을 사용하여 데이터에서 의미 있는 통찰력을 발굴하고 의사결정에 올바르게 적용하실 수 있는 분 엔지니어링 팀을 리드해본 경험이 있으신 분 PO/PM 혹은 그에 준하는 경험이 있으신 분 영어로 실시간 비즈니스 의사소통을 능숙하게 수행하실 수 있는 분 Hiring Process 고용 형태: 정규직 채용 절차: 서류전형 > 사전 과제 > Live Coding Interview > 기술 면접 > 기술 심화 및 Cultural Alignment 면접 > 최종합격 (* 필요 시 전형이 변경 진행될 수 있습니다.) 서류 전형의 경우 합격자에 한하여 개별 안내드릴 예정입니다. 지원 서류: 자유 양식의 상세 경력기반 영문이력서(PDF) 이 포지션은 전문연구요원 현역 편입/전직, 전문연구요원 보충역 편입/전직 채용이 가능합니다. 병역특례요원의 경우, 병역특례 관련법에 따라 복무관리를 진행합니다.

-Legal Entity: Hyperconnect -Brand: Match Group AI Match Group AI 소개 Match Group AI는 온라인 데이팅의 핵심 문제를 AI로 해결하는 중앙 기술 조직입니다. 프로필, 매칭, Trust & Safety 등 사용자 경험의 핵심 영역에 집중해 과제를 발굴합니다. 이를 최신 AI 기술과 데이터 기반의 접근을 통해 해결하여 사용자 경험을 혁신합니다. 나아가 Tinder, Hinge 등 Match Group 내 글로벌 데이팅 브랜드와의 협업을 바탕으로 그룹 공통 기술 기반을 확장합니다. 팀에서 진행 중인 프로젝트를 더 자세히 알고 싶으시다면, 다음 게시글을 확인해보세요. 👉 "Match Group AI 팀을 소개합니다." Match Group AI ML Team 소개 ML Team은 Match Group의 다양한 서비스에 AI/ML 기술을 적용하는 ML Engineer로 구성된 팀입니다. 팀의 업무는 실제 제품이 만들어지고 운영되는 과정에서 발생하는 문제를 발굴하고 정의하는 것에서 시작됩니다. 문제 해결에 가장 적합한 SotA(State-of-the-Art) 모델을 개발하거나 재현하고, 완성된 모델을 모바일과 서버 환경에 안정적이고 효율적으로 배포합니다. 이후에도 지속적인 모니터링과 개선을 통해 서비스의 AI Flywheel을 구축해 나갑니다. 이 과정에서 백엔드/프론트엔드/DevOps 엔지니어, 데이터 분석가, PM 등 다양한 전문 조직과 긴밀히 협업하며 실제 사용자에게 임팩트를 주는 AI 경험을 만들어갑니다. 일하는 모습에 대한 조금 더 자세한 이야기는 다음의 내용을 참고하시면 좋습니다. - [How AI Lab Works] Head of AI - Shurain 인터뷰 - AI in Social Discovery(Blending Research and Production) 업무 성과 중 일부는 논문 또는 오픈소스 코드로 외부에 공개하기도 합니다. 제품에 사용하기 위한 목적으로 ML 모델을 만들다 보면, 기존 연구로는 부족한 경우가 많습니다. 부족한 부분을 채우기 위해 진행된 연구의 결과물을 프로젝트 참여자들이 모두 함께 협업하여 연구의 의미 있는 부분을 정돈하고, 가능하다면 코드와 함께 공개합니다. 그 결과, 지금까지 아래 성과를 포함해 약 20여 건의 대외적 연구 성과를 거둘 수 있었습니다. - 2024년 CUPID: 1:1 소셜 디스커버리 플랫폼을 위한 실시간 세션 기반 상호 추천 시스템 ICDM Workshop 발표 - 2023년 TiDAL: 효율적인 학습 과정의 모델 행동에 기반한 액티브 러닝 기법 ICCV 2023 게재 - 2023년 모더레이션 환경에서 여러 분류 기준을 동시에 만족하기 위한 문턱값을 잡는 연구 WSDM 2023 게재 - 2022년 대화 생성에서의 의미적 다양성을 높이는 연구 EMNLP 2022 게재 - 2022년 레이블 노이즈가 심한 환경에서 효과적으로 학습하는 방법 ECCV 2022 게재 - 2022년 타깃 캐릭터의 몇가지 발화만을 이용하여 타깃 캐릭터를 모방하는 챗봇연구 NAACL 2022 게재 - 2022년 대화 생성 모델에서 예시를 활용하여 성능을 높이는 연구 ACL 2022 Workshop 발표 - 2022년 모바일 환경에서 오디오 분류를 위한 distillation 기술 연구 ICASSP 게재 ML 연구가 잘 진행되기 위해서는 딥러닝 학습을 위한 인프라도 잘 갖추어져야합니다. ML engineer들이 충분히 모델 개발 및 실험을 진행할 수 있도록 자체 딥러닝 클러스터(총 160대 A100 GPU, 40대 H100 GPU)를 보유하고 있습니다. 추가적으로, 데이터 수집, 전처리를 포함한 자체적인 데이터 파이프라인을 클라우드 서비스를 활용하여 구축 및 운영하고 있습니다. 또, ML 모델의 제품화를 도와주실 다양한 software engineer(백엔드/프론트엔드/DevOps/MLSE)분들과 함께 일하고 있습니다. 담당 업무 Match Group AI의 ML Engineer는 최신 AI/ML 기술을 연구하고 적용하는 과학자이자, 실제 서비스 환경에 맞게 모델 및 시스템을 설계하고 최적화하는 엔지니어입니다. 조직에서는 다음과 같은 다양한 업무들을 담당하고 있으며, 이들 중 적어도 한 가지 업무에 대해서 능숙하게 수행하실 수 있는 분을 찾고 있습니다. - Match Group에서 서비스 중인 제품들에서 발생하는 다양한 비즈니스 문제를 해결합니다. 비즈니스 문제의 배경, 목표, 제약사항 등을 파악하여 올바른 AI/ML 문제로 재정의하고, 이를 해결하기 위한 최선의 방법론을 찾아 문제를 해결하는 과정을 수행합니다. - Match Group의 새로운 제품 및 기능 개발에 참여합니다. 아이디어 구상부터 프로토타이핑, 실제 사용자에게 도달하기까지의 과정에서 AI/ML 기술들을 활용하여 제품 및 기능을 빠르고 올바르게 구현하는 과정을 수행합니다. - 대규모 언어 모델(large language models) 및 멀티모달 모델(multimodal models) 선행연구 및 개발을 담당합니다. Match Group의 데이터와 최신 기술들을 활용하여 데이팅 도메인에 특화된 모델을 만들고, 제품의 다양한 영역에 적용시킬 수 있는 방법을 찾아가는 과정을 수행합니다. 위 업무들을 수행하기 위해 ML Engineer에게는 탄탄한 AI/ML 기초 지식뿐만 아니라 최신 기술을 빠르게 학습 및 도입할 수 있는 적응력을 기대합니다. 이러한 역량을 기반으로 주로 다음과 같은 기술적인 주제들을 접하게 됩니다. - 텍스트, 이미지, 이벤트 로그 등을 포함하여 다양한 modality를 가진 데이터를 올바르게 다루는 방법 및 수집된 데이터의 편향 및 노이즈를 올바르게 해소하는 방법 - 제품의 목표와 정렬된 평가 지표 정의 및 목표 달성을 위한 최적의 모델 학습 방법 - 엔지니어링 제약사항 및 인프라 환경을 고려한 온디바이스 및 대규모 모델 추론 최적화 방법 Requirements AI/ML에 대한 기본 지식과 적어도 한 개 이상의 특정 도메인에 대한 깊이 있는 지식을 갖추고, 관련 프로젝트 경험이 있으신 분 Exploratory Data Analysis(EDA)를 통해, 데이터의 통계적 특성과 패턴을 발견하고 이를 문제 해결 과정에 반영하실 수 있는 분 PyTorch, Tensorflow, JAX 등의 오픈소스 프레임워크 및 전반적인 파이썬 개발에 능숙하신 분 ML 모델 학습 파이프라인 구축 및 배포 과정에 필요한 엔지니어링 역량을 갖추신 분 AI 기술의 서비스화에 관심이 많으신 분 한국어로 원활한 의사소통이 가능하고, 영어로 작성된 기술문서 이해, 작성 및 비대면 소통이 가능하신 분 학위/국적 무관 Preferred Qualifications AI/ML 관련 탑티어 학회 및 저널(NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, KDD, …) 게재 실적 혹은 관련 대회 수상 실적이 있으신 분 대규모 학습 데이터와 컴퓨팅 자원을 활용하여 LLM 혹은 비슷한 규모의 모델을 학습시켜 성능을 끌어올린 경험이 있으신 분 클라이언트(Android, iOS), 백엔드를 포함해 AI 분야 외 프로젝트 개발 경험이 있으신 분 실제 서비스에 AI 기술을 적용하여 주요 비즈니스 지표를 유의미하게 향상시켜 본 경험이 있으신 분 A/B 테스트 실험 기획 및 타겟 KPI 지표를 정의하고, SQL기반 데이터 분석을 진행한 경험이 있으신 분 영어로 실시간 비즈니스 의사소통을 능숙하게 수행하실 수 있는 분 Hiring Process 고용 형태: 정규직 채용 절차: 서류전형 > 사전과제 > Live Coding Test 면접 > 기술 면접 > 기술 심화 및 Cultural Alignment 면접 > 최종합격 (* 필요 시 전형이 변경 진행될 수 있습니다.) 서류 전형의 경우 합격자에 한하여 개별 안내드릴 예정입니다. 지원 서류: 자유 양식의 상세 경력기반 영문이력서(PDF) 이 포지션은 전문연구요원 현역 편입/전직, 전문연구요원 보충역 편입/전직 채용이 가능합니다. 병역특례요원의 경우, 병역특례 관련법에 따라 복무관리를 진행합니다.

Match Group is seeking a Principal AI Learning & Enablement Partner to accelerate how AI is adopted and applied across the organization. This role operates at the intersection of learning, AI, and business operations – connecting brand-level innovation with scaled enablement across Match Group. You will partner closely with AI Brand Leads, business leaders, and functional teams to translate emerging AI capabilities into practical, role-specific applications that drive meaningful behavior change. This role plays a critical part in ensuring employees move beyond basic usage to more advanced, integrated applications of AI in their day-to-day work. This is not a traditional L&D role. It is a cross-brand enablement role focused on surfacing what’s working, scaling best practices, and accelerating adoption across a decentralized ecosystem. This role may travel to key hub cities including Dallas, LA, New York, Vancouver, and Paris. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best teams, so we work together IRL in our LA, Palo Alto, or San Francisco office 3 days/week. What You'll Do Shape the AI Enablement Strategy This role is built for someone who wants to actively shape how AI takes hold at Match Group — not just execute on requests, but bring real perspective. You'll help translate business priorities into a phased, practical roadmap and be a genuine thought partner in how the company moves forward. Develop and maintain a company-wide AI learning roadmap, identifying where AI can create the most meaningful business impact across functions Serve as a strategic advisor to business leaders, translating organizational goals into targeted AI priorities and phased adoption plans Synthesize signals from across the business (adoption trends, emerging tools, team needs) to inform and evolve the enablement approach Influence how Match Group thinks about AI as a competitive capability — not just a productivity tool — and help shape the long-term vision for an AI-native workforce Partner with Business Leaders to Drive Impact Build trusted relationships with senior leaders and functional teams to understand their highest-priority challenges and co-create AI solutions Lead discovery and scoping conversations that translate business problems into concrete AI use cases with measurable outcomes Act as the connective tissue between technical capabilities and business needs — bridging what's possible with what matters Drive alignment across stakeholders on adoption priorities, sequencing, and success metrics Build & Activate AI Capability Across the Organization Own the middle layer of AI enablement — the space between introductory training and deep technical work — where real behavior change and workflow integration happen Design and deliver hands-on learning experiences grounded in real business scenarios, moving employees from basic usage to advanced, integrated application Build scalable resources — playbooks, prompt libraries, workflows, toolkits — that embed AI into how people actually work Close adoption gaps by identifying underutilizing teams and designing targeted enablement approaches, especially for non-technical employees and late adopters Partner with and coordinate internal SMEs and external vendors to deliver high-quality, role-specific training— this role enables teams to execute, it doesn’t deliver everything itself Track adoption and impact: usage, behavior change, and efficiency gains Embed AI into Talent & Culture Identify strategic opportunities to integrate AI into core Talent & Culture programs, including learning, performance management, and career development Influence how Talent Management and Learning programs are redesigned with AI at their core — from AI-supported goal setting and development planning to feedback and coaching Experiment with and scale AI-enabled solutions that meaningfully improve efficiency and employee experience Inform Tool Adoption (Advisory Role) Provide the AI Council with practical, employee-centered perspectives on AI tool adoption, scalability and usability based on business needs and employee experience Advise on pilot and rollout strategies by bringing an enablement, adoption and employee experience perspective to tool evaluations Gather and synthesize employee feedback, usage insights and adoption considerations to help inform rollout decisions and guidance Translate the capabilities of vetted, approved tools into practical workflows, use cases and enablement resources Build the AI Enablement Ecosystem Architect and maintain a centralized AI enablement hub — a go-to resource for best practices, self-serve learning, and just-in-time guidance Curate external learning resources and vendor partnerships that accelerate capability building at scale Ensure content evolves continuously alongside new tools and internal use cases Champion Responsible AI Partner with Legal, IT, and Communications to ensure all enablement includes clear, practical guidance on responsible and safe AI use Align enablement efforts with internal governance and tool policies Support structured onboarding and learning rollout for new AI tools Measure What Matters Define and track adoption, engagement, and effectiveness metrics that demonstrate business impact — not just activity Use data and feedback to continuously sharpen the strategy and improve the quality of enablement Share insights and adoption trends with senior leaders to inform ongoing investment decisions What We're Looking For 7+ years of experience spanning AI, product, strategy, enablement, learning & development, or a related field — with a track record of driving organizational change Demonstrated experience influencing senior leaders and driving cross-functional alignment without formal authority Experience building or owning a strategic roadmap — whether for a product, capability, or organizational initiative Hands-on fluency with AI tools (e.g., Claude, Glean, Cursor, Gemini, ChatGPT) and a genuine curiosity about what's coming next Strong ability to translate technical capabilities into business language and practical, role-specific workflows Proven ability to move organizations from awareness to adoption — identifying gaps, designing interventions, and measuring outcomes Comfortable operating in ambiguity as a first-of-kind role; able to build structure and momentum without a pre-existing playbook Experience working with or enabling technical teams (e.g., Engineering, Product, Data) alongside business and people functions Nice to Haves Experience working in a multi-brand or global tech organization Background in engineering, AI systems, digital transformation, or learning strategy Familiarity with agentic frameworks, automation tools, or AI workflow design

At Match Group, we help millions of singles find meaningful relationships through our diverse portfolio of brands, including Match, Hinge, OkCupid, Plenty of Fish, and more. With a presence in over 190 countries, our reach is expansive-and growing fast. We're driven by a shared mission: to spark connections that transform lives. From behavioral science to machine learning, we apply deep thinking and innovative approaches to the complex dynamics of human connection. The Team: Match Group’s Analytics Team is looking for a Manager, Data Science with expertise in Marketing Analytics. Our analytics team serves the entire MG portfolio, acting as a center of excellence to standardize how we measure marketing performance across all our global brands, including industry leading names like Tinder and Hinge. We develop a deep understanding of the business and its data and establish close working relationships with all teams—including Product, Marketing, Engineering, and Finance—so that we can provide recommendations and make proposals regarding prioritization of initiatives, marketing investments, A/B tests, and company performance. As a data scientist in this role, you will partner with Marketing teams at each of our BUs, providing support through advanced marketing measurement techniques, A/B test design and analysis, and data-driven insights through ad-hoc analyses, presentations, and automated reports and dashboards. This role will require extensive experience with SQL, data wrangling, statistics, econometric modeling, and A/B testing, as well as the ability to work in cross-functional teams to drive actionable insights. Experience with user growth and acquisition across multi-brand consumer products is a plus. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life. We think the same is true for creating the best platforms, so we work together IRL in our Vancouver office three days a week. How you’ll make an impact: Master and be responsible for analyzing marketing responsiveness and business performance. Build industry leading models to better understand the impact of our marketing investments and strategy. Find the “story” in the data and communicate insights and opportunities with executives and other internal teams. Perform ongoing ad-hoc analyses to evaluate the efficiency of our marketing spend, build reporting infrastructure, and assist in making budgeting recommendations to optimize growth. Integrate captured event data with other customer data sources, such as surveys, ad publishers, and app store data. Support reporting and business intelligence needs of the entire Marketing team. We may be a Match if: 5+ years of relevant experience in analytics or data science. Experience building Marketing Mix Models (MMM) and other marketing measurement techniques Ability to think objectively and interpret meaningful themes from quantitative and qualitative data Ability to build constructive and effective relationships with a broad and diverse group of business partners Ability to execute complex analytics projects where you have to determine salient questions in a complex ecosystem and present action-oriented conclusions Proficiency with SQL required Strong coding capabilities, preferably in Python. Familiarity with BI concepts and tools, including data pipeline orchestration and dashboard creation Knowledge of statistics, specifically experimental design and analysis Proficiency in Microsoft Excel and Microsoft PowerPoint (or equivalents). Familiarity with common marketing KPIs, metrics, and growth experiments

エウレカは、真剣にパートナーを見つけたいと考えている方に向けた恋活・婚活マッチングアプリ「Pairs（ペアーズ）」を運営している会社です。Pairsは、日本国内で累計登録数2,700万以上の日本最大級のオンラインデーティングアプリです。 2012年に日本版、2013年に台湾版・2025年に韓国版のサービス開始して以来、順調に成長を続け、70万人以上の方がPairsでお相手を見つけています。 2015年には、世界中でオンラインデーティングアプリを展開するMatch Groupにジョインし、私たちのミッションである「人生に”あってよかった”と思ってもらえるものを」を実現するため、日本国内だけでなく、グローバルで更なる成長を目指しています。 "Pairs" is an online dating app used by more than 27 million people in Japan. Since launch, we have received success stories from over 700,000 people. Pairs is the first-in-market to offer 24/7 in-house customer service, including text and image monitoring, so our users' safety and security are always ensured. We created Pairs to help singles discover new and interesting ways to find a life partner. In the United States, more than 1 in 3 couples met their partner online. In Japan more than 70 per cent of singles claim to have no partner. As the number 1 online dating service in Japan, we're working hard every day to help singles find their true love. Since the release of Pairs in October 2012, we have helped to create opportunities for many users to find partnership and marriage. Pairs offers the opportunity to find an ideal partner that matches one's own values through various search features and a well-developed MyTag. Everyday, we receive many reports of successful matches from across Japan and from overseas. It is our hope that more and more opportunities for wonderful relationships will be created, which inspires us to improve our service every day. 実際の業務内容： 機械学習エンジニアとして、推薦や検索、自然言語処理や画像処理とその結果がシステムの中心となるプロジェクトに携わり、特に embeddings / vector search を用いた近傍検索、candidate generation / retrieval を含む推薦・検索機能の改善を通じて、プロダクトチームがそれらの結果を正しく利用し継続的にプロダクト指標を改善できる状態に導くこと。 As a Machine Learning Engineer, you will work on projects where recommendation, search, NLP, and image processing are core to the system. You will focus on improving recommendation and search functionalities - particularly through embeddings, vector search, and retrieval (including candidate generation) - to enable product teams to effectively leverage these outputs and continuously drive product KPI improvements. 仕事の進め方： 機械学習エンジニアとしてプロダクトチームと共に企画段階からコミットし、プロジェクトの目的や評価指標、何をアウトカムとするかの定義からプロダクトマネージャを支援する。 ハンズオンで分析及び概念実証を行い、プロトタイプを作成し、プロジェクト内外の理解を深めることで説明責任を果たしながら成功確率を高める。 性能（精度、UXに影響する処理速度等）、コスト、リードタイムなどのトレードオフを意識しながら機械学習コンポーネントを含むシステムの技術選定、設計、実装を行う。 専用の情報共有ツールを使って、ノウハウや議事録などの情報共有を行う。 Partner closely with product teams from the early planning stage, supporting Product Managers in defining project objectives, success metrics (KPIs) and expected outcomes; Conduct hands-on analysis and proof of concept (PoC), build prototypes, and drive alignment across stakeholders by improving understanding of the solution and its impact; Design and implement ML-driven systems while balancing trade-offs across model performance (e.g., accuracy, latency impacting UX), cost and development lead time; Actively document and share knowledge, insights, and decisions using internal collaboration tools (e.g., documentation, meeting notes). 仕事から得られるもの： 国内最大級のオンラインデーティングサービスのデータを用いた機械学習を含む機能開発に企画段階からプロダクトチームと共に関わり、最適解を実現する為の過程で得られる知識とテクニカルスキル エウレカでは技術選択は全て開発者が責任を持って行っており、必要に応じて最新技術も積極的に活用できます サービスマインドの高いメンバーが多く、あるべき姿を議論しながら、調整力、コミュニケーションスキル、解決力を磨くことができます 定性・定量のバランスを持った高度で本質的なデータ・ドリブン組織の推進・実務経験 外資系IT企業ならではの論理性、圧倒的スピードのある開発経験 英語力（親会社や外国籍メンバーとのコミュニケーションのため英会話学習をサポート。また、社内メンバーの国籍は、台湾・韓国・フィリピン・オーストラリア・イギリス・アメリカ・フランスと多種多様です） 開発を通した社会貢献（晩婚化問題、少子化問題、人生における自由な選択etc.） Work on machine learning–driven feature development using data from one of the largest online dating services in Japan, collaborating with product teams from the planning stage, and gaining knowledge and technical skills through delivering optimal solutions; At Eureka, engineers own technology decisions and are empowered to adopt the latest technologies as needed; Work with highly service-oriented team members and develop coordination, communication, and problem-solving skills through discussions on best practices; Gain hands-on experience in driving a truly data-driven organization that balances both qualitative and quantitative perspectives; Experience the logical thinking and high-speed execution typical of a global IT company; Improve English skills through communication with global stakeholders and international team members across Match Group(English learning support is provided); Contribute to social impact through product development (e.g., addressing declining birth rates, delayed marriage, and enabling freedom of choice in life). 使っているツール/環境： 開発言語：Python, Go データベース：BigQuery, Amazon Aurora MySQL, DynamoDB, Redis, Elasticsearch データ処理：Google Cloud Vertex AI, Google Cloud Dataflow, Cloud Pub/Sub, Fluentd, Fluent Bit, dbt, Dagster etc BIツール：Tableau, Superset 構成管理：Terraform 開発、その他：GitHub, Slack, Jira, Confluence Google Workspace 各種ツール：Google Sheets / Google Slides AIツール：ChatGPT Enterprise / Codex, Claude Code, Cursor, Gemini(Google Workspace),, その他自社開発AIツール及びプラグイン LLM運用 / 監視：Langfuse(self-hosted) インフラ：AWS/Google Cloud 各種サービス Programming Languages: Python, Go Databases: BigQuery, Amazon Aurora MySQL, DynamoDB, Redis, Elasticsearch Data Processing: Google Cloud Vertex AI, Dataflow, Pub/Sub, Fluentd, Fluent Bit, dbt, Dagster, etc. BI Tools: Tableau, Superset Infrastructure as Code: Terraform Development & Collaboration: GitHub, Slack, JIRA, Confluence Google Workspace: Google Sheets, Google Slides AI Tools: ChatGPT Enterprise, Codex, Claude Code, Cursor, Gemini (Google Workspace), as well as internally developed AI tools and plugins LLM Observability / Monitoring: Langfuse (self-hosted) Infrastructure: AWS and Google Cloud services 必須条件： 次の一般的なソフトウェアエンジニアとしての知識・スキル・経験を持っていること： WebApp開発の知識、スキル 一般的なWebアプリケーションの大まかな構成を理解していること バックエンドアプリケーションの開発経験（設計及び実装）（Sr. Backend engineer レベルであると尚可） ルタイムの業務経験最低2年以上 General knowledge, skills, and experience as a software engineer, including: Web application development Understanding of overall web application architecture; Backend development experience (design and implementation); experience at a Senior Backend Engineer level is a plus; Minimum of 2 years of full-time professional experience. 検索、推薦のいずれかをコアコンポーネントとするソフトウェア開発プロジェクトに携わり、継続的に改善した経験： 例: ECにおける商品推薦、ジョブマッチングにおける求人票 / 候補者推薦、embeddings / vector search を用いたユーザーベクトル近傍検索による推薦改善、candidate generation / retrieval の改善、Elasticsearch / Solr を用いた検索システムの結果改善をはじめとした運用 コンポーネント自体はマネージドサービスでも可 経験ドメインがtwo-sided marketであると尚可 Experience working on and continuously improving software systems where search or recommendation is a core component: E.g.,product recommendation in e-commerce, job/candidate matching, improving recommendations using nearest-neighbor retrieval over user embeddings/vector, optimizing candidate generation/retrieval, and improving search results using Elasticsearch/Solr; Managed services are acceptable for these components; Experience in a two-sided marketplace domain is a plus. ビジネス/エンジニアとのコミュニケーション力： プロジェクトのアウトカムをプロダクトマネージャや分析者、場合によっては経営層と共に議論し定義でき、機械学習タスクとして要件に落とし込める 複雑な要件を整理、適切なステークホルダーを巻き込んで合意形成できる S trong communication skills with both business and engineering stakeholders: Ability to define project outcomes in collaboration with Product Managers, analysts, and, when needed, leadership, and translate them into machine learning requirements; Ability to structure complex requirements, engage relevant stakeholders, and drive alignment. 日本語ビジネスレベル以上 ネイティブでない方であれば、日本語が共通言語であるチームでの業務経験があることが望ましい 日本がメインの領域となるグローバルプロダクトであるため、日本の言語や文化に対する理解を深める意向を持つことが望ましい Japanese proficiency at business level or above: For non-native Japanese speakers, experience working in a Japanese-speaking environment is preferred; Given that Japan is the primary market, a willingness to deepen understanding of Japanese language and culture is preferred. 英語ビジネスレベル以上 Match Groupの英語話者ステークホルダーとのコミュニケーションや英語による正式な資料作成が可能なレベル、もしくはそこまでキャッチアップ可能であること English proficiency at business level or above: Ability to communicate with English-speaking stakeholders within Match Group and create formal documentation in English, or the ability to ramp up to that level. 歓迎条件： 特に1. 2. の要件を重視します。 1. 機械学習プロジェクトをリードした経験： 複数の職種が関与する機械学習プロジェクトにおけるテクニカルリード 曖昧で制約が自明でない状況においてトレードオフを明確にした技術的意志決定（性能、コスト、UXを考慮した要素技術の選定・設計） ミドルマネジメントと共に、現場と経営層の認識を揃えながらプロジェクトを推進 2. 統計学、機械学習、A/Bテストの知識、スキル： 各種統計検定に関する理解があり、ケースに応じて適切な利用と結果の解釈ができる 機械学習（主に教師あり学習）の評価指標を理解している 機械学習、検索、推薦、自然言語処理、画像処理等の高度なアルゴリズムを用いる幅広い分野関する知識と経験を持ち、プロジェクトの目的に対してこれらから最適なタスクへの落とし込みができる embeddings / vector search を用いた近傍検索、candidate generation / retrieval を含む推薦・検索システムの設計・改善経験を持つと尚可 A/Bテストについて網羅的な知識を持ち、プロジェクトKPI/KGIとテストターゲット、機械学習のドメインとの関連を理解した上で設計及び実施、評価が出来る。 NumPy, Pandas, SciPy, Matplotlib, scikit-learn, TensorFlow, PyTorchなど機械学習に関わるライブラリ・フレームワークの本番環境における利用経験 3. データ処理におけるテクニカルスキル： SQL言語の知識、技能 Python/Ruby/Java/Scala/Goいずれかの業務での開発経験 ストリーム/バッチのデータ処理経験 RDB以外のデータベース利用経験 4. MLサービスの運用経験： Kubeflow / TFX などMLパイプラインの開発・運用 Kubernetes の監視・運用 SageMaker, Vertex AI などの managed ML services を利用したML基盤の開発・運用 Langfuse を用いた LLM アプリケーションの observability / tracing / monitoring の運用経験 5. 機械学習チームをリードした経験： チーム内の担当領域のロードマップの策定（四半期から1年） 機械学習チームと事業及び企業全体のロードマップとの整合性をとる為の継続的な行動 6. データエンジニアリングスキル： リアルタイムでオンライン推論が行われる機械学習パイプラインの設計・構築・運用・監視経験 BigQuery / DynamoDB 等を用いて、用途に応じた特徴量管理基盤（feature store 相当）を設計・構築・運用した経験 高トラフィックサービスのストリームデータ処理経験 大規模データのバッチ処理経験 データに対する要求水準が高い Application のデータストアやデータパイプラインの設計、技術選定の経験 7. 大規模システムの運用経験、トラブルシューティングスキル： ボトルネックを特定し、適切な解決策を提案、実施した経験 または運用を事前に考慮して、高可用性、高信頼性のあるシステムを設計した経験 SREのプラクティスを改善しながら運用する経験（SLO, エラーバジェット等） We place particular emphasis on requirements 1 and 2. 1. Experience leading machine learning projects: Technical leadership in ML projects involving multiple functions Ability to make technical decisions under ambiguity, clearly identifying trade-offs (e.g., performance, cost, UX) and selecting/designing appropriate solutions Experience aligning on goals across teams and driving projects in collaboration with mid-level management and leadership 2. Knowledge and skills in statistics, machine learning, and A/B testing: Understanding of statistical methods and ability to apply and interpret them appropriately Understanding of ML evaluation metrics (primarily for supervised learning) Broad knowledge and experience across areas such as ML, search, recommendation, NLP, and computer vision, with the ability to translate project goals into appropriate technical approaches Experience designing and improving recommendation/search systems, including embeddings, vector search, and candidate generation/retrieval, is a plus Strong understanding of A/B testing, with the ability to design, execute, and evaluate experiments in alignment with KPIs/KGIs and ML domains Hands-on experience with ML libraries/frameworks in production (e.g., NumPy, Pandas, SciPy, Matplotlib, scikit-learn, TensorFlow, PyTorch) 3. Technical skills in data processing: Proficiency in SQL Development experience in at least one of the following: Python, Ruby, Java, Scala, or Go Experience with stream and batch data processing Experience working with non-relational databases 4. Experience operating ML services: Experience developing and operating ML pipelines (e.g., Kubeflow, TFX) Experience monitoring and operating Kubernetes Experience building and operating ML platforms using managed services (e.g., SageMaker, Vertex AI) Experience operating LLM applications with observability / tracing / monitoring tools (e.g., Langfuse) 5. Experience leading ML teams: Experience defining team-level roadmaps (quarterly to annual) Experience aligning ML initiatives with broader business and organizational roadmaps 6. Data engineering skills: Experience designing, building, operating, and monitoring ML pipelines for real-time (online) inference Experience designing and operating feature store–like systems using BigQuery, DynamoDB, etc. Experience with stream processing for high-traffic services Experience with large-scale batch data processing Experience designing data stores and pipelines for applications with high data requirements 7. Experience operating large-scale systems and troubleshooting: Experience identifying bottlenecks and proposing/implementing appropriate solutions Or experience designing systems with high availability and reliability, considering operational aspects Experience improving and operating with SRE practices (e.g., SLOs, error budgets) 欲しい人物像： 担当領域に関して、物事を前に進めるためのあらゆることを実践できる 経営陣、法務、プライバシー保護担当等のプロジェクトチーム外のステークホルダーとのコミュニケーションを必要に応じて行える 自身の仕事の価値を理解してやりきれる力がある 最新技術を常にキャッチアップし、必要に応じてプロダクトに導入できる あるべき理想を持ちながら、現実問題に落とし込んでギャップを埋める議論ができる Ability to proactively drive work forward within their area of ownership; Ability to communicate effectively with cross-functional stakeholders (e.g., leadership, legal, privacy teams) as needed; Strong ownership and the ability to understand and deliver the value of their work end-to-end; Continuously stays up to date with the latest technologies and is able to apply them to the product when appropriate; Ability to bridge the gap between ideal solutions and real-world constraints through practical and constructive discussions.

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role: We are hiring a Staff Machine Learning Engineer to drive the design, development, and deployment of machine learning systems that power our core products in User Growth and Monetization. You will work on building deeply personalized user experiences that drive business growth and meet individual needs and preferences: by targeting the right users with the right products at the right time, we empower product teams to drive meaningful growth in revenue and user engagement while enhancing user satisfaction. The Growth Product Group is responsible for the development of paid features to accelerate dating outcomes, driving engagement throughout a user’s lifecycle and making Hinge the dating destination for all communities. You can expect to work on a broad range of problems, from identifying how to send the right message to the right user at the right time to optimizing the efficacy of our paid offerings. This is a fast growing team, and you will help define the vision and strategy that drives meaningful growth and accelerates machine learning at Hinge. Responsibilities: Lead the end-to-end development of production-grade ML systems such as user targeting models that will help drive engagement, improve dating outcomes and/or improve user adoption of and engagement with paid features Define and own the technical roadmap for ML within your product area and align with company-wide priorities Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally Keep abreast of and bring to Hinge applicable cutting-edge research, technologies, and best practices in the ML/AI space. Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale. Ensure the ethical and responsible use of ML/AI and compliance with privacy regulations to protect user data Communicate effectively to audiences of various technical and non-technical backgrounds What We're Looking For: Strong programming skills: Proficiency in Python and ML libraries such as PyTorch Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning machine learning for user facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus. System design & architecture: Strong background in setting up and optimizing ML infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for ML (e.g., model versioning, automated testing). Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow, or Weights & Biases is a plus. Data engineering knowledge: Skills in handling and managing large datasets including data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow. Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.. Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes. 5+ years of experience, depending on education, as an MLE, with at least 2 years in a senior or staff-level role Previous experience in User Growth or Monetization 3+ years of experience designing and developing end-to-end, production grade ML systems 4+ years of experience working in a cloud environment such as GCP, AWS, Azure 3+ years of experience leading projects with at least 1 other team member through completion. A degree in computer science, engineering, or a related field or equivalent experience.

At Meetic, a Match Group company, we have been pioneering online dating for over 20 years, leveraging our legacy to reinvent how people meet, connect, and build meaningful relationships across Europe and beyond. As part of Match Group, we manage a diverse portfolio of brands, from iconic names like Meetic, Match, Lexa, and LoveScout24 to tailored experiences for singles over 50, OurTime, DisonsDemain, Zweisam, and single parents, Even and Stir. Our teams design, build, and scale platforms across 20+ countries and 18 languages, ensuring every single finds the right match for their lifestyle and lifestages. We also drive growth for other Match Group brands like The League, OkCupid, Archer, Salams, and Plenty Of Fish, expanding our impact worldwide. When it comes to dating, the connection starts online, but the real magic happens once you meet in real life (IRL). We think the same is true for creating the best platforms, so we work together IRL 3 days / week in our office, 53 rue de Chateaudun, 75009 Paris. Role overview Meetic relies heavily on data to drive and grow its business. As a truly data-driven company, data is our daily compass for decision-making. The Data Science team sits at the crossroads of the company’s key challenges and makes the most of all the information contained in our data. It is part of Meetic’s Data Hub, which brings together all data-related stakeholders (Data Science, ML/Data Engineers, DBAs, BI). Our extensive data ecosystem allows us to work across all business domains: profile recommendations for our users, pricing and optimization of business models, optimization of subscription and à-la-carte offers, customer knowledge and experience, detection and prevention of harmful behaviors, marketing analytics and acquisition, image processing …and much more! Our technical environment: Google Vertex AI, BigQuery, Elasticsearch, Kafka, Scala, Python, Spark, SQL, AWS… We are looking for creative and passionate people who enjoy working in a dynamic and international environment. How you’ll make an impact Design, implement and maintain ML pipelines and CI/CD workflows (training, deployment, retraining) Expose ML models through inference APIs (Cloud Run / Vertex AI Endpoints) to be consumed by product and platform services Own monitoring, alerting and reliability of ML systems in production Write optimized, high-performance, scalable, testable, maintainable, and observable code Ensure operational readiness of ML models (logging, metrics, incident handling) Act as a local ML Ops reference for the Paris Data Science team Support DS teams to industrialize models and experiments Collaboration Work in an international, multi-brand environment with teams across Europe, Canada, and the US Collaborate with platform teams (Canada/US) on GCP infrastructure and standards Work closely with Product squads of E&E brands to understand and address their needs Collaborate with Data Engineers and software architect to ensure data quality and production deployment of models Promote a data-driven culture in a highly collaborative environment and share your expertise Technology watch Stay up to date with advances in machine learning and devops to apply best practices within the company We could be a match if You hold an engineering degree or a Master’s degree in mathematics or computer science, with at least 5–6 years of experience in similar roles You enjoy discovering scientific methods and challenging existing ones, always keeping in mind the ultimate goal of improving the performance of our dating application Experience with ML / DevOps practices Experience with CI/CD, Docker, Terraform Experience with monitoring & alerting (Cloud Monitoring, Grafana, etc.) Understanding of IAM, service accounts, security Comfortable operating ML systems in production Strong understanding of model serving constraints Comfortable communicating in English during meetings Excellent analytical and synthesis skills Excellent written and verbal communication skills Rigorous scientific mindset Team spirit and proactive attitude Curiosity and strong learning ability Nice to have Experience working with Kafka or event-driven architectures in ML or data platforms Knowledge in A/B test framework Knowledge in QlikView, Looker, or other BI tools

Hinge is the dating app designed to be deleted In today's digital world, finding genuine relationships is tougher than ever. At Hinge, we’re on a mission to inspire intimate connection to create a less lonely world. We’re obsessed with understanding our users’ behaviors to help them find love, and our success is defined by one simple metric– setting up great dates. With millions of users across the globe, we’ve become the most trusted way to find a relationship, for all. About the Role Joining Hinge’s AI Platform Core team offers a unique opportunity to help define and contribute to a growing ML platform with the goal of accelerating the development, deployment, and operations of AI enabled features on Hinge. If you are excited by the idea of working on challenges ranging from maturing and scaling our training and serving frameworks to enable millisecond real-time predictions and models that continually learn at scale, providing automatic model performance monitoring & debug frameworks, and building Platforms for architecting and evaluating generative AI & Agentic systems, then read on! With a mission to democratize AI for Hinge, ensuring it remains easily accessible, robust, scalable, cost-efficient, and trusted. You'll collaborate across our product and platform teams, as well as with external stakeholders like data science and backend engineering. You will meet our internal customers where they are and optimize towards their needs by delivering value incrementally and coupled to their problems. Being part of a small yet impactful team means having a broad scope of responsibility. As the role of ML at Hinge is only becoming more impactful, this role provides you with a chance to grow in impact and scope across the company. This is an exciting opportunity to own and help define the future of machine learning within a growing and maturing team! Responsibilities Own or contribute to technical designs for the feature platform, training platform, serving platform, AI observability platform and GenAI Platform and underlying operational infrastructure to enable Product impact. Develop, maintain, and enhance frameworks for AI/ML model development and deployment while establishing and driving best practices in MLOps / GenAI engineering. Design, advocate, and implement for usability, reliability, scalability, operational excellence, and cost management while delivering incrementally. Collaborate closely with ML Engineers, Data Scientists, Data Engineers and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process. Mentor and educate ML Engineers, Backend Engineers and Product Managers on current and up and coming tools and technologies for ML operations & GenAI product development through presentations and documentation. Help design and architect an AI platform that adheres to the principles of responsible AI (Authenticity, Transparency, Equity) and builds privacy compliance into the platform. Lead build vs buy discussions on technologies that underpin the Platforms we serve. Participate in on-call and Incident Management processes. What We're Looking For 4+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems. 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes 1+ year of experience leading projects with at least 1 other team member through completion. 1+ year of experience for Senior designing and developing online and production grade ML systems. A degree in computer science, engineering, or a related field. Strong programming skills: Proficiency in languages like Python, Go, or Java. System design & architecture : Ability to design scalable and efficient ML systems. Cloud platform proficiency : The ability to utilize cloud environments such as GCP, AWS, or Azure. ML knowledge : A basic understanding of ML algorithms, techniques, and best practices. Data engineering knowledge : Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage Collaboration and communication skills : The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.. Strong written communication : The ability to communicate complex ideas and technical knowledge through documentation Software leadership skills : A track record of leading projects through completion with quantifiable and measurable outcomes.