[About 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 for the technologies created by AI Lab to contribute to the actual growth of products by stably providing them through mobile and cloud servers and solving the challenges encountered. Under this goal, Hyperconnect AI Lab has been developing machine learning technologies that contribute to Hyperconnect's products, including Azar, for several years.

[About Machine Learning Software Engineer]

Hyperconnect Machine Learning Software Engineers (MLSEs) apply machine learning technology to services that connect people through software engineering, innovating user experience. They apply numerous models across various domains, including video/audio/text/recommendation, created by the in-house AI organization to production, solve engineering problems encountered while stably providing them through mobile and cloud servers, and aim for the technologies we create to contribute to the growth of actual services.

Under this goal, Hyperconnect's ML Software Engineers have been developing machine learning technologies that contribute to various Hyperconnect products, including Azar and new internal services, for several years. They are also developing technologies to easily utilize these accumulated technologies in various global business services of Match Group, an S&P 500 company.

ML Software Engineers aim to apply all our AI technologies to products to create business impact and develop sustainable systems to accelerate the application of AI technology. To achieve this goal, they (1) develop scalable backend servers based on ML models, (2) develop/operate real-time data pipelines for ML model inference, (3) develop systems to support various operations involved in ML model production and operation, and (4) develop platforms that can be commonly managed in collaboration with other teams. In this process, they work closely with other roles and proactively carry out all necessary processes (problem definition, hypothesis setting, experiment design, analysis, and feedback) to achieve KPIs. For more detailed stories about how we work, please refer to the following:

• AI Platform Dev Team Interview
• ML Application Team Interview
• Head of AI - Shurain Interview
• AI in Social Discovery (Blending Research and Production)

Interesting problems that ML Software Engineers are solving are uploaded on the Tech blog.

• Calculating Real-time ML Feature Data using ksqlDB
• 10 Python Performance Optimization Tips for High-Performance ML Backend
• Reducing Machine Learning Model Serving Costs by 1/4
• Operating an Event-Driven Live Streaming Recommendation System
• Data Storage Technologies for Machine Learning Applications

[Work Environment]

[Top-tier AI Organization in Korea]

Work with Machine Learning Engineers and Machine Learning Research Scientists who regularly publish papers at top-tier AI/ML conferences. You can check out papers published by Hyperconnect here.

[Rich MLOps Know-how]

Work in an organization with rich MLOps know-how, using over 50 models in production.

• Deview 2023 - Implementing a Feature Store for Real-time Recommendation Systems
• Deview 2021 - How to Deploy More Models Faster?
• Building a High-Performance Deep Learning Cluster Part 1

Responsibilities

• Hyperconnect is making various efforts to apply machine learning technology to its products. Hyperconnect's ML Software Engineers primarily perform the following tasks:

[Recommendation System Development]

• Develop personalized recommendation systems for an enjoyable experience in Azar's core 1:1 video call feature. Also focus on developing recommendation and search systems in various other areas. These systems are designed with significant consideration for performance to enable real-time operation at a global scale, and the microservices operated by the team handle some of the highest traffic levels within the entire company.
• Additionally, develop real-time data pipelines for real-time model inference, utilizing Apache Flink and KSQL to process real-time events and provide data sources for models. Design systems (e.g., streaming applications, Feature Store) for fast and stable collection, processing, and serving of features, and also perform tasks to discover new features that can improve model performance.

[Moderation System Development]

• Develop moderation systems and platforms as part of Trust & Safety for a safe environment for customers. Build AI-based content and behavior monitoring systems to effectively detect inappropriate content and spammers.
• Furthermore, develop platforms for producing and managing labeling data to continuously improve moderation models. Build Golden Data Sets to provide high-quality data for model training, and also design features to deploy Human Factors instead of AI when needed for the service. This supports AI models in maintaining a reliable level of quality while solving various layers of moderation problems.

[AI Feature Development Acceleration Strategy Support]

• Build an AI Flywheel system to provide various functions for hypothesis testing, model training (continuous learning), evaluation, management, and deployment.

Requirements

• Comfortable speaking and reading English, and able to handle daily tasks in English.
• Possess 3+ years of Software Engineering experience or equivalent skills.
• Possess solid fundamental knowledge in CS fundamentals (Operating Systems, Computer System Architecture, Data Structures, and Algorithms).
• Proficient in one or more languages among Java, Kotlin, Golang, Python, Javascript (Typescript), and able to quickly learn and use other programming languages.
• Have an understanding of various NoSQL databases and experience designing services using RDBMS.
• Proficient in using SQL for data exploration and able to understand the meaning of investigated data and use it as a basis for problem-solving.
• Enjoy moving across various functional stacks and can quickly adapt to unfamiliar environments.
• Possess strong communication skills to collaborate with stakeholders from various roles.

Preferred Qualifications

• Those who enjoy researching new technologies or challenging tasks.
• Experience using frameworks such as FastAPI, SpringBoot, NestJS.
• Experience with open source contributions and operating forked repositories.
• Understanding and experience applying asynchronous network frameworks and technologies.
• Interest in MSA-based system architecture design.
• Understanding and experience applying the advantages and design benefits of Event Sourcing / CQRS.
• Experience from machine learning model training to service deployment.
• Experience integrating AI technology into actual services and significantly improving key metrics.
• Experience leading an engineering team.
• Experience developing backend servers that handle large-scale traffic.
• Experience developing distributed/parallel systems for large-scale/real-time processing such as Spark, Flink.

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 change if necessary.)
• For document screening, only successful candidates will be notified individually.
• Application Documents: Free-form detailed English resume (PDF)