Machine Learning Engineer Intern (Data-Search-TikTok Recommendation) - 2026 Summer (BS/MS)

Team Introduction Our Search Engineering Team is responsible for building and owning TikTok's search engine which can provide our users with the best search experience. In TikTok Search Team, you’ll have the opportunity to build full-stack search engine system and combine information retrieval technology with modern machine learning methods from related fields such as NLP, CV, and recommender system. We embrace a culture of self-direction, intellectual curiosity, openness, and problem-solving.

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: • Content understanding: Applying the industry's cutting-edge NLP and CV technology and leveraging LLM to match the most relevant videos for each search query, and continuously improve the relevance of TikTok search. • User Behavior Modeling: solving the recommendation problem in search, let TikTok search increase the ability of personalization on the basis of "relevant", and understand users better. • Video understanding: comprehensive use of NLP, CV, as well as LLM for better video understanding from the perspective of the video itself and social network, improve authority, credibility, and usefulness of search results.