About the team
Interest-based E-commerce is a new and fast growing business that aims at connecting all customers' interests to excellent sellers and high quality products on TikTok Shop.
Different from other traditional E-commerce platforms, TikTok Shop provides customers with personalized and unique shopping experience through E-commerce live-streaming and E-commerce short videos.
The recommendation system plays an extremely important role in helping customers explore their shopping interests.
We are a group of applied machine learning engineers and research scientists that focus on E-commerce video/live-streaming recommendations on the major traffic source of TikTok ForU page, where we serve traffic for billions of users every single day.
We develop innovative algorithms and ML techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions.
We are interested and excited about applying large scale machine learning to solve various real-world problems in E-commerce and recommendation.
Responsibilities
- Participate in building large-scale (10 million to 100 million) live-streaming and short video e-commerce recommendation algorithms and systems on TikTok.
- Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg.
Click Through Rateand Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
- 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 and develop various strategies using ML technology to improve user shopping experience, and resolve e-commerce business challenges, such as the cold start problem and traffic allocation.
- Design and build supporting/debugging tools as needed.