Machine Learning Engineer Intern (Search E-commerce) - 2025 Summer (PhD)

Team Introduction
The Search E-Commerce team is responsible for the search algorithm for TikTok's rapidly growing global e-commerce business. We use state-of-the-art large-scale machine learning technology, the cutting-edge NLP, CV and multi-modal technology to build the industry's top-class search engine to provide the best e-commerce search experience, for more than 1 billion monthly TikTok users around the world. Our mission is to build a world where "there is no hard-to-sell good-priced product in the world".

We are looking for talented individuals to join us for an internship in 2025. Internships at TikTok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at TikTok.

Internships at TikTok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. This Internship Program runs for 12 weeks beginning in May/June 2025. Successful candidates must be able to commit to one of the following summer internship start dates below:

Monday, May 12
Monday, May 19
Tuesday May 27 (Memorial Day May 26)
Monday, June 9
Monday, June 23

We will prioritize candidates who are able to commit to these start dates. Please state your availability clearly in your resume (Start date, End date).

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities
-Improve the basic search quality and user experience: Optimize query analysis and text relevance matching. Understand e-commerce video content and implement multi-modal matching. Improve users' perception of product authority, and deeply participate in the design and implementation of core search products. Comprehensively improve the end-to-end shopping experience from browsing to after-sales.
- Design and implement the end-to-end ranking system (recall, first stage ranking, final stage ranking and mixed row): Improve users' personalized shopping interests model. Improve the shopping conversion efficiency for merchandise, video and live stream to promote GMV growth.
- Promote the development of the ecosystem: From the perspective of the industry and businesses, solve challenging problems such as supply and demand matching, business cold start, and sustainable business growth, etc. Think, analyze and adjust the evolution of the system to achieve long-term and sustainable growth of GMV.