쿠팡 · 채용 중 635건
Staff, Machine Learning Engineer - Coupang Play
Staff, Machine Learning Engineer - Coupang Play
머신러닝 엔지니어정규직리드 · 7년 이상
쿠팡플레이에서 추천 및 개인화 시스템을 고도화할 Staff급 머신러닝 엔지니어를 채용합니다. 머신러닝 제품 개발 7년 이상의 경력과 추천 시스템 도메인 경험이 필수입니다. 대규모 데이터 기반의 모델 설계, 학습 및 프로덕션 배포를 주도하며, 제품 팀과 협업하여 최상의 사용자 경험을 제공하는 역할을 수행합니다.
Coupang Play is an OTT video streaming service. We offer a wide range of content, including Original Series, TV Shows, Movies, and Sports.
We are in the business of delivering exceptional storytelling. We produce award-winning scripted and unscripted Original Series, such as “Boyhood”, “Anna”, and “SNL Korea”. We believe Sports can also be elevated by creative storytelling. Play is now an authoritative destination for Sports, broadcasting K-League, La Liga, Bundesliga, F1, NFL, and other leading leagues and tournaments. We also offer some of the latest movies that were just released in local theaters, bringing more exceptional storytelling to our customers.
To ensure that we deliver all content seamlessly, we invest in Engineering, Product, and Design. Coupang Play is available on all mobile devices, tablets, PC, Smart TV, Apple TV, and Android TV. Our teams keep optimizing how customers discover and stream both VOD and Live content. That includes investments in frontend, backend, and infrastructural developments. We also embrace Machine Learning to refine our CX.
We are still very early in our growth stage. If you enjoy solving complex problems using industry-leading technology or unconventional business approaches, please join us. We are building a world-class team of problem-solvers, all dedicated to delivering exceptional storytelling to our customers.
As a Staff Machine Learning (ML) Engineer at Coupang Play, you will work on our large and increasing catalog of content including streaming video on-demand titles, live events, as well as transactional video on-demand titles. You will mine large amount of playback data to gain insights from OTT subscriber behavior; create data jobs to generate machine learning features; build, train and improve new or existing models; participate in building the framework used for contents recommendations and personalization by leveraging all available data sources, machine learning tools, and other technical platforms. You will work closely with our Product and Engineering teams to build our recommendation and personalization systems.
Application Review - 1st Interview - 2nd Tech Interview - Leadership interview – Offer
The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.
Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.