Introduction to AI Lab

Hyperconnect AI Lab innovates user experience by identifying and solving problems in products that connect people, which are difficult to approach with existing technologies but can be solved through machine learning.

To achieve this, we develop numerous models across various domains including video/audio/natural language/recommendation, and aim to contribute to the growth of actual products by stably providing them through mobile and cloud servers and solving the problems encountered.

Under this goal, Hyperconnect AI Lab has been developing machine learning technologies that contribute to Hyperconnect's products, including Azar, for several years.

Introduction to ML Research Engineer Role

An ML Research Engineer at AI Lab requires research capabilities as a scientist who researches and improves cutting-edge models, and engineering capabilities as an engineer who makes technical decisions considering realistic constraints.

Based on these capabilities, they discover/listen to problems encountered in actual products, translate them into AI problems, and solve them.

In this process, they actively collaborate with and receive help from various specialized organizations such as backend/frontend/DevOps/ML software engineers, data scientists/analysts, and PMs. For more detailed stories about how we work, please refer to the following:

• AI in Social Discovery (Blending Research and Production)
• [[How AI Lab Works] Head of AI - Shurain Interview](https://career.hyperconnect.com/post/64d1ffea7b0f3d00016fc4e7)

ML Research Engineers view the product problem-solving process as a research process. They proactively take charge of the entire problem-solving process from problem definition, stakeholder persuasion, goal setting, deriving SotA models, schedule management, performance analysis, to future strategy setting.

They define priorities considering user needs and business impact, and contribute to product growth from an AI perspective with a long-term vision.

Some of the work results are sometimes published externally as papers or open-source code. When creating ML models for product use, existing research is often insufficient. To fill the gaps, the results of the research are collaboratively organized by all project participants, and if possible, published with code. As a result, we have achieved about 20 external research achievements, including the following:

• 2024: CUPID: Real-time Session-based Mutual Recommendation System for 1:1 Social Discovery Platforms presented at ICDM Workshop
• 2023: TiDAL: Active Learning Method based on Model Behavior in Efficient Training Process published at ICCV 2023
• 2023: Research on setting thresholds to satisfy multiple classification criteria simultaneously in a moderation environment published at WSDM 2023
• 2022: Research on increasing semantic diversity in dialogue generation published at EMNLP 2022
• 2022: Effective learning method in environments with severe label noise published at ECCV 2022
• 2022: Chatbot research mimicking a target character using only a few utterances from the target character published at NAACL 2022
• 2022: Research on improving performance by utilizing examples in dialogue generation models presented at ACL 2022 Workshop
• 2022: Research on distillation technology for audio classification in mobile environments published at ICASSP

To solve business problems with AI, a proper deep learning training infrastructure must also be in place. At Hyperconnect, we have built and utilize our own deep learning research cluster to enable ML Research Engineers to sufficiently develop and experiment with models.

Various on-premise equipment, including a total of 160 A100 GPUs and multiple H100 GPUs, can be used for research and development. Additionally, we have built and operate our own data pipeline, including data collection and preprocessing, using cloud services.

Introduction to Contents Understanding Role

The contents understanding role at AI Lab focuses on extracting useful information for business by taking unstructured data consisting of video, images, audio, and natural language as input, with the primary goal of contributing to Trust & Safety operations through moderation (Interview). We contribute to understanding content generated by Hyperconnect and Match Group brands and perform various tasks in collaboration with Match Group to meet global Trust & Safety standards. To this end, we are very interested in dealing with the following AI problems:

• Lightweight model design and optimization techniques that can achieve high accuracy while maintaining short latency and low power consumption in mobile and web environments.
• Active learning, core-set selection, semi-/self-supervised learning methods for tracking and managing label quality in data with severe noise and imbalance, and securing performance with minimal labeling.
• Optimizing multi-task or multi-label classification within a limited parameter budget, and modeling techniques that integrate multi-modal information such as text, images, and video.
• Domain adaptation to overcome distribution differences between domains, and meta-learning techniques for service scalability.
• Learning methods to ensure Fairness and Privacy to meet international AI standards.
• Streaming-based modeling to detect or predict abnormal behavior such as spam and fake accounts in real-time using user behavior logs and content analysis information.
• LLM utilization methods to innovate ML production processes.

Requirements

• Understanding of the overall AI/ML domain and in-depth knowledge of at least one specific domain, with 3+ years of related project experience.
• Ability to continuously learn proactively to build and maintain the team's technical competitiveness, including AI/ML.
• Ability to solve engineering constraints that cannot be solved by conventional methods, based on AI modeling capabilities and a deep understanding of software engineering.
• Experience integrating AI technology into actual services and significantly improving key metrics.
• Strong communication skills to collaborate with stakeholders from various job functions.
• Experience quickly and accurately implementing papers whose implementations are not publicly available from scratch.
• Ability to take responsibility for and drive the entire project process to maximize business impact even in highly uncertain and complex problem situations.
• Deep understanding of causal analysis (DID, RCT, Causal Inference, etc.), multivariate testing, and sequential testing.
• Experience in A/B test experiment planning, defining target KPI metrics, and performing SQL-based data analysis.
• Fluent communication in Korean.

Preferred Qualifications

• Ph.D. in AI/ML or Master's degree in AI/ML with 5+ years of relevant work experience.
• Publications in top-tier machine learning conferences and journals (NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, KDD, ...) or awards in AI-related competitions.
• Experience achieving State-of-the-Art (SotA) on publicly available benchmark datasets.
• Extensive development experience outside the AI/ML field, including client (Android, iOS) and backend.
• Experience participating in open-source development related to machine learning.
• Ability to boast extensive knowledge across the entire AI/ML domain.
• Experience leading an engineering team.
• Experience as a PO/PM or equivalent.
• Fluent communication in English.

Employment Type/Recruitment Process

• Employment Type: Full-time
• Recruitment Process: Document Screening > Coding Test/Assignment > 1st Interview > Recruiter Call > 2nd Interview > Final Offer (* The process may be added or changed if necessary.)
• For document screening, only successful candidates will be notified individually.
• Application Documents: Free-form detailed English resume based on career (PDF)
• This position is available for Specialized Research Personnel (전문연구요원) active duty transfer/conversion, and Specialized Research Personnel (전문연구요원) supplementary service transfer/conversion. For military service exemption personnel, service management will be conducted according to relevant military service exemption laws.