Tech Lead Manager - Growth & User Lifecycle Optimization, Global E-commerce Content Recommendation

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

- Design and optimize growth strategies for user acquisition, engagement, and retention; build lifecycle segmentation and personalized recommendation models.

- Develop cross-domain and multimodal models that connect short videos, live streams, and e-commerce to build unified user interest graphs.

- Apply advanced algorithms (Graph Learning, Contrastive Learning, Diffusion Models) to enable interest transfer and cross-scenario optimization.

- Explore generative and representation learning methods to enhance cold-start performance, recommendation diversity, and conversion efficiency.

- Lead core growth projects, collaborate with product and operations teams, and drive algorithmic innovations to global scale.