About HYPERCONNECT AI
HYPERCONNECT AI is an organization that creates real business results through machine learning research. We redefine business problems as ML problems and focus on improving the core metrics of our products. We avoid simply applying external solutions; instead, we rethink problem formulation from the start and build the best options ourselves. As a result, HYPERCONNECT AI's research has led to real business impact, and a few of these cases have been featured in the Earnings Letter of our parent company, Match Group. At HYPERCONNECT AI, research is a means, not an end; the goal is clear business impact. Examples of problems we are solving at HYPERCONNECT AI include: Recommendation model scaling: Scaling laws are widely accepted for LLMs, but not for recommendation models. We research architectures where performance improves together with model and data scale. We work with large models, including LLMs, while designing models that account for realistic constraints such as inference. Recommendation model alignment: Every recommendation problem ultimately comes down to quantitatively measuring "how much will the business grow if we show this content to this user." We research methods to post-process scaled recommendation models and align them with business objectives, based on reinforcement learning, causal inference, game theory, and related areas. ML process automation for moderation using multimodal LLMs: In Trust & Safety (moderation) ML processes spanning multiple modalities such as images, audio, and text, we research how to automate and advance steps previously performed by humans, including data labeling, model improvement, and evaluation, using LLMs. We focus on making LLMs accurately understand the company's internal data and policies, and on making their judgments reliable at the level of internal experts. Some meaningful research results are published externally as papers or open-source projects. We do not aim for paper publication, but when solving product problems we encounter aspects that existing research does not fully explain, and we document them as research outcomes (for reference). So far, we have produced about 20 external research outcomes. To solve business problems with AI, the infrastructure for model training must also be properly in place. At HYPERCONNECT AI, we have built and use our own research GPU cluster so that ML Research Scientists can fully carry out model development and experimentation.
Staff, Machine Learning Research Scientist (Azar) At Azar, which connects people with people, we solve problems that are difficult to address with existing technology but can be solved with machine learning. We work on Azar's Recommendation, Moderation, and problems in new areas of innovation, with the goal that the technology we create contributes to real growth of the service.
Responsibilities As a core partner to the Head of AI, you define the key problems to be solved in Azar's recommendation and moderation areas. You align the perspectives of multiple functions and leadership, and together decide organizational priorities and execution strategy based on business impact and technical feasibility. Based on the latest AI/ML research and technical advances, you discover opportunities that are difficult to uncover through existing product requirements alone. You propose how new technologies could transform Azar, and judge whether they are worth investing in based on technical and business feasibility and long-term technical moats. You redefine business requirements as AI/ML problems and design solutions by broadly considering deep learning as well as classical machine learning, statistics, optimization, and more. Rather than repeatedly adding heuristics and exception handling to meet business requirements, you draw on a deep understanding of problem structure and ML theory to explore directions close to the theoretical optimum within given constraints. You dive directly into unsolved, high-difficulty ML problems and urgent business problems. Beyond offline model performance, you verify changes in user experience and live service metrics to create long-term product and business results. You understand the entire ML system in which data, training, evaluation, experimentation, serving, and feedback are connected, not just the performance of individual models. From an ML perspective, you design the overall system structure and the role of each component so that fast, reliable experimentation and continuous improvement are possible. You align research topics and solution directions with other ML Research Scientists and lead each research effort toward a unified technical strategy and product results. You collaborate with Machine Learning Software Engineers, data engineers, product organizations, and others, and drive technical decisions so that research results are applied to real products.
Requirements A PhD in machine learning or a related field, or equivalent research capability More than 5 years of experience developing and improving machine learning-based products or services in industry Experience defining ambiguous business problems as clear ML problems and designing solution strategies based on theoretical grounding and experiments Experience proactively solving complex, difficult problems to completion, and connecting model performance to improvements in user experience or business metrics Ability to collaborate with other researchers, engineers, and non-technical functions, align technical direction, and persuasively lead decisions
Preferred Qualifications Experience as first author of a paper in the main track of at least one top-tier AI/ML conference (such as NeurIPS, ICLR, ICML, CVPR, KDD) Research experience in core technologies related to recommendation systems, reinforcement learning, LLMs, and AI Agents Experience productizing and repeatedly improving ML systems in user-facing services that handle large-scale traffic Ability to lead technical discussions with global teams in English
Hiring Process Employment type: Full-time Hiring process: Document screening > Recruiter call > 1st interview > 2nd interview > 3rd interview > Final acceptance (*The process may be added to or changed as needed.) Required documents: Freeform resume (PDF) detailing career history, in Korean or English
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If any false information is found in the submitted materials, or if there is a legal disqualification for employment under applicable law, the hiring may be cancelled, and additional screening and document verification may be conducted beyond the hiring process announced in advance if necessary. Candidates who are eligible as national veterans will be given preference in accordance with relevant laws; if applicable, please indicate this when applying and submit supporting documents upon hiring. If you apply for a position at HYPERCONNECT, this privacy policy applies to the handling of personal information: https://career.hyperconnect.com/privacy
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