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Software Engineer Intern; Video-on-Demand Algorithm - Summer; BS/MS
Job in
San Jose, Santa Clara County, California, 95111, USA
Listed on 2026-02-17
Listing for:
Tiktok
Seasonal/Temporary, Apprenticeship/Internship
position Listed on 2026-02-17
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
The Video-on-Demand (VoD) Algorithm team is responsible for optimizing the app experience related to performance for Tik Tok users. This team collaborates with all teams within and beyond the VoD team, to provide algorithmic support for end-to-end optimization solutions, including video shooting, uploading, processing, delivery, and playback, etc.
We are looking for talented individuals to join us for an internship in 2026. Internships at Tik Tok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at Tik Tok.
Internships at Tik Tok 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. It runs for 12 weeks.
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 Byte Dance / Tik Tok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible. 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
Responsibilities
* Design and implement media streaming-related algorithms to improve Tik Tok user experience. Examples include video preloading, adaptive bitrate streaming (A ), network bandwidth estimation, CDN traffic allocation, and transport-layer algorithms, to name a few.
* Contribute to end-to-end media ecosystem design to improve the algorithmization of the multimedia system.
* Run controlled experiments on millions of streaming sessions to validate improvements.
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