Machine Learning Engineer Intern (TikTok Search Local Services) - 2026 Summer (BS/MS)

Team Introduction The Data-Search-TikTok-Local Services team enhances local services by improving user discovery of hospitality, dining, and leisure experiences while driving ecosystem growth. They leverage large-scale machine learning to refine search and recommendation systems, focusing on personalized relevance, CTR/CVR prediction, and optimized conversion efficiency for billions of users.

We are looking for talented individuals to join us for an internship in 2026. Internships at our Company aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at our Company. Internships at Our Company aims 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. 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 Our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). Summer Start Dates: May 11th, 2026 May 18th, 2026 May 26th, 2026 June 8th, 2026 June 22nd, 2026

This internship provides students the opportunity to join one of our engineering teams where you will have the opportunity to: Participate in the improvement of the search core algorithm, possible directions include:

1. Large Language Model Application: Building AI search for local service needs by deploying and posttraining LLMs, constructing robust and comprehensive RAGs.
2. Content understanding/matching: Applying the industry's cutting-edge LLM technology to match the most relevant videos for each search query, and continuously improve the understading of queries and relevance of TikTok search.
3. User Behavior Modeling: solving the recommendation problem in search, letting TikTok search increase the ability of personalization on the basis of "relevant", and understand users better.