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

Responsibilities

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
- 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.

Qualifications

Minimum Qualifications
- 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.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $198360 - $416100 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.