Sr. Machine Learning Software Engineer (Match Group AI)

Key Skills

Aartificial intelligence, C++, Common data structures, Data science techniques, Deep Learning, Design, Kotlin, Machine learning techniques, Python Programming, Swift

Job Description

Hyperconnect Machine Learning Software Engineer (MLSE) applies machine learning technology to services and innovates user experiences through software engineering in services that connect people. We apply numerous models in various domains, including images/voices/texts/recommendations created by the in-house AI organization, to production, and solve engineering problems encountered while stably providing them through mobile and cloud servers, and we aim to ensure that the technology we create contributes to the growth of actual services.

Under this goal, Hyperconnect's ML Software Engineer has been developing various types of machine learning technologies for several years, and is thinking about and developing ways to effectively apply these accumulated technologies to products.

ML Software Engineer aims to apply all the AI ​​technologies we have to products to create business impact and develop sustainable systems to accelerate the application of AI technologies. To achieve this goal,

• Develop basic code to enable the successful application of ML models on-device.
• We perform performance optimization of the entire pipeline from preprocessing media to inferring with ML models.
• Measure the online performance of machine learning models and improve user experience through A/B testing.
• Develop a scalable backend server based on ML models.
• Develop/operate real-time data pipelines for ML model inference and evaluation.

In the process, I work closely with other departments and proactively participate in all processes (problem definition, hypothesis setting, experimental design, analysis, and feedback) required to achieve KPIs. For more detailed information about my work style, please refer to the following content.

• 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 also uploaded to the Tech blog .

• Compute real-time ML feature data using ksqlDB
• 10 Python Performance Optimization Tips for High-Performance ML Backends
• Reduce machine learning model serving costs by 1/4
• Operating an event-based live streaming recommendation system
• Data storage technology for machine learning applications

[Work environment]

[Domestic top-level AI organization]

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

[Rich MLOps Know-How]

You will work in an organization with a wealth of MLOps know-how, with over 50 models in production.

• Deview 2023 - Feature Store Implementation for Real-Time Recommendation Systems
• Deview 2021 - How to deploy more models faster?
• Building a high-performance deep learning cluster, Part 1

[Duties]

Hyperconnect is working in various ways to apply machine learning technology to its products. Currently, Hyperconnect's ML Software Engineer will perform the following tasks:

[Development of client SDK with machine learning capabilities]

We develop and operate machine learning SDKs embedded in Hyperconnect's mobile applications using tools such as TFLite. The SDKs developed by Hyperconnect must be able to operate on numerous devices used around the world, so they are developed with a very high target of stability. In addition, they must be able to be continuously updated to provide the highest-performance machine learning API. Furthermore, we understand how they can affect the performance and user experience of the entire app, and optimize/modify the related code.

[Inference Optimization]

In addition to efficiently serving machine learning instances of Hyperconnect, it may be necessary to identify and optimize bottlenecks during inference. This can be done at various levels, such as hardware optimization, model optimization, and optimization by deployment target. We are actively utilizing profiling, etc., and detecting various data signs that are logged to identify and resolve these issues.

[Machine Learning-Based Backend Application Design and Implementation]

We develop various machine learning-based backend services (JVM, golang, python) to improve the quality of services operated by Hyperconnect and Match Group. They are designed with a lot of consideration from a performance perspective to enable real-time operation on a global scale, and the microservices operated by the team are handling the highest level of traffic within the company.

Requirements

• Someone with more than 2 years of experience in mobile client development or equivalent skills
• Those who have a solid foundation in CS fundamentals (operating systems, computer system architecture, data structures, and algorithms)
• Those who are proficient in at least one of the following languages: Java, Kotlin, Swift, and those who can learn and develop other programming languages ​​such as Python, Go, and JavaScript.
• A person who is proficient in using SQL required for data analysis, understands the meaning of existing data, and can use it as a basis for solving actual business problems.
• Someone who enjoys moving across multiple stacks with diverse development experience and can quickly adapt to unfamiliar environments
• Someone who can quickly define new problems and proactively solve them
• Someone with a positive and respectful teamwork attitude who can work together and ask for help from each other to achieve common goals.
• Anyone interested in machine learning and deep learning

Preferred Qualifications

• Anyone with more than 1 year of experience using ML/DS libraries or deep learning frameworks using Python
• Someone with more than 1 year of experience developing libraries using C++
• Anyone with experience from training machine learning models to deploying services
• Someone who has experience working with multiple development organizations
• Experience designing, building, and debugging Linker, Loader, and system-level low-level libraries.
• Experience deploying models in production-level mobile and web environments and dealing with the various layers of issues that can arise there
• People who have no difficulty communicating in English