Job Description

DepartmentEngineering and Technology
LevelExperienced (Individual Contributor)
LocationSingapore The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed the most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do. Browse our Engineering and Technology team openings to see how you can make an impact with us.

About the Team:
Our team builds the recommendation engine at Shopee, and strives to provide the best online shopping experience for our users. You will have the chance to interact with a fast-growing user base and item base, participate in the end-to-end optimization of our recommender system, as well as develop the most advanced business solutions. If you love technologies as much as we do, then welcome to join and together we can build the first-rate recommendation engine.
:

• Responsible for designing multiple context-aware recommender systems in e-commerce.
• Drive improvement of core modules like recall/ranking/rerank in the recommender system by technology.
• In-depth understanding of e-commerce to optimise recommendation strategies like customer lifecycle analytics and cold start solutions, so as to consistently take the impact of our recommendation to the next level.

• Minimum 5 years of hands-on experience in search/personalised recommendation/advertising or related fields.
• Familiar with recommender system business, with specialisation in one or multiple areas: ranking, reall, user profiling, content understanding.
• In-depth understanding of machine learning algorithms such as LR/GBDT/DNN and large-scale sparse feature modeling, and deep learning frameworks (e.g. tensorflow, caffe, mxnet, pytorch)
• Good data structure and strong coding skills. Familiar with Linux, and at least one of the programming languages: C++, Go or Python.
• Preferably to have working experience in e-commerce search, recommendation and advertising.