We are a group of applied machine learning engineers and data scientists that focus on general feed recommendations and E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems.
What you will do:
• Participate in building large-scale (10 million to 100 million) recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc in TikTok.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.
• Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data
pipelines, feature engineering, model optimization and innovation.
• Design and build supporting/debugging tools as needed.
In order to enhance collaboration and cross-functional partnerships, among other things, at this time, our organization follows a hybrid work schedule that requires employees to work in the office 3 days a week, or as directed by their manager/department. We regularly review our hybrid work model, and the specific requirements may change at any time.
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