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 definitions from the start and build the best choices ourselves. As a result, HYPERCONNECT AI's research has led to tangible business impact, and a small number of these cases have been featured in the Earnings Letter of our parent company, Match Group. At HYPERCONNECT AI, research is a means rather than an end, and 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 conduct research to build structures where performance improves as model and data size grow. We work with large models, including LLMs, while designing models that account for practical constraints such as inference.
Recommendation model alignment: Every recommendation problem ultimately comes down to quantitatively measuring "how much the business will grow when this content is shown to this user." Based on reinforcement learning, causal inference, game theory, and related fields, we research how to post-process scaled recommendation models to align them with business goals.
Automating moderation ML processes with multimodal LLMs: In Trust & Safety (moderation) ML workflows spanning images, audio, text, and other modalities, we research how to automate and advance steps previously performed by humans, such as 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.
Significant research results are sometimes published externally as papers or open-source projects. We do not aim for paper publication, but when existing research does not fully explain issues encountered while solving product problems, we organize and document these as research outcomes (for reference). To date, we have produced about 20 external research outcomes.
To solve business problems with AI, the infrastructure for model training must also be properly established. 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 problems in Azar's Recommendation, Moderation, and new areas of innovation, with the goal of ensuring the technology we create contributes to real service growth.
Responsibilities
As a key partner to the Head of AI, you will define the core problems to be solved in Azar's recommendation and moderation areas. You will align the perspectives of various functions and leadership and, based on business impact and technical feasibility, jointly decide the organization's priorities and execution strategy.
Based on the latest AI/ML research and technological advances, you will discover opportunities that are difficult to identify from existing product requirements alone. You will propose how new technologies can innovate Azar and decide whether they are worth investing in, based on technical and business feasibility and long-term technical moats.
You will redefine business requirements as AI/ML problems and design solutions by broadly examining deep learning as well as classical machine learning, statistics, optimization, and more. Rather than repeatedly adding heuristics and exception handling just to meet business requirements, you will explore directions close to the theoretical optimum within given constraints, based on a deep understanding of problem structure and ML theory.
You will dive directly into unsolved, high-difficulty ML problems and urgent business problems. Beyond offline model performance, you will validate changes in user experience and live service metrics to create long-term product and business results.
You will understand the entire ML system, where data, training, evaluation, experimentation, serving, and feedback are connected. You will design the overall system structure and the role of each component from an ML perspective to enable fast, reliable experimentation and continuous improvement.
You will align research topics and solution directions with other ML Research Scientists and guide each research effort toward a unified technical strategy and product outcomes. You will also collaborate with Machine Learning Software Engineers, data engineers, and product organizations, leading technical decisions so that research results are applied to real products.
Requirements
Ph.D. in machine learning or a related field, or equivalent research experience 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 grounds and experimentation 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
First-author main-track publication at one or more top-tier AI/ML conferences (such as NeurIPS, ICLR, ICML, CVPR, KDD) Research experience in core technologies related to recommender systems, reinforcement learning, LLMs, and AI agents Experience productizing and iteratively improving ML systems in user-facing services handling large-scale traffic Ability to lead technical discussions with global teams in English
Hiring Process
Employment type: Full-time Recruitment process: Application review > (if applicable) job competency essay* > Recruiter call > First interview > Second interview > Final offer (the process may be added to or changed as needed.)
*During application review, we may request answers to job-competency questions to confirm job fit.
Application documents: Free-form, detailed career-based resume in Korean or English (PDF)
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If any false information is found in the submitted materials or if there is a legal disqualification from providing labor, the hiring may be cancelled. Additional screening and document verification may be conducted beyond the hiring process announced in advance, if necessary.
Veterans of national service (국가보훈대상자) receive preferential treatment under applicable laws. If you fall under this category, please notify us when applying and submit supporting documents upon hiring.
When applying for positions at HYPERCONNECT, this Privacy Policy applies to the handling of personal information: https://career.hyperconnect.com/privacy
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