Tech Lead Manager - Growth & User Lifecycle Optimization, 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
- 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.

Qualifications

Minimum Qualifications
- Bachelor's degree or higher in Computer Science or related fields.
- Strong background in recommender systems, cross-domain modeling, or generative recommendation.
- Proficient in TensorFlow/PyTorch and familiar with GNNs, Diffusion, or multimodal learning.
- Solid understanding of e-commerce growth metrics and user lifecycle strategies.
- Excellent analytical, communication, and leadership skills.

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.