Tech Lead Manager - E-commerce Live Streaming & Traffic Ecosystem, Global E-commerce Content Recommendation

Tiktok • Seattle, WA

61d

About The Position

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.

Requirements

• Bachelor's degree or higher in Computer Science or related fields.
• Strong foundation in recommendation systems, with expertise in Ranking, Matching, Long-term Modeling, Multi-objective Learning, User Fatigue Modeling, Reinforcement Learning, or Lifelong Engagement.
• Proficient in TensorFlow or PyTorch, with experience designing complex ranking or value optimization strategies.
• Deep understanding of content commerce, live streaming ecosystems, and user behavior patterns.
• Strong analytical and data sense with the ability to design metrics, conduct experiments, and perform attribution analysis.
• Proven experience leading projects or teams, driving cross-functional alignment and end-to-end execution.

Responsibilities

• Define and optimize the long-term value system for e-commerce live streaming by balancing short-term conversion with long-term retention and user experience.
• Build user experience health models to guide traffic allocation toward high-quality, high-value content.
• Develop negative interest and fatigue modeling mechanisms to identify user disinterest, content saturation, and exposure fatigue across multiple scenarios (live, short video, shelf).
• Design dynamic decay and penalty mechanisms to personalize fatigue modeling and improve content diversity.
• Model topic-level user affinity and long-term engagement through multi-task learning and long-term value optimization, promoting sustainable creator-user relationships.
• Establish ecosystem health indices and data-driven governance loops for continuous optimization of traffic and supply dynamics.
• Lead core ecosystem and user experience projects from problem definition to algorithm deployment and performance evaluation, collaborating closely with product, strategy, and operations teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Industry: Broadcasting and Content Providers
• Number of Employees: 5,001-10,000 employees