Job Description
Team Introduction:
The Recommendation Infrastructure Team is responsible for building reliable, efficient, flexible, and generic recommendation components, systems, and platforms. Our work supports the fast iteration and incubation of TikTok's recommendation products.
We are looking for talented individuals to join us for an internship in 2026. 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 August/September 2026. Successful candidates must be able to commit to one of the following internship start dates below:
(Select below options for Fall)
- 8/10/2026
- 8/24/2026
- 9/8/2026
- 9/21/2026
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:
This internship provides students the opportunity to join one of our engineering teams where you will have the opportunity to:
• Build and maintain high performance online services for TikTok recommendation system.
• Build extremely efficient and reliable data pipelines for candidates generation, profile generation, training examples generation, realtime online training, etc.
• Work with your mentor to build globalized large-scale recommendation system.
• Design and develop high performance computing frameworks and storage systems.
