Software Engineer Intern (TikTok Search Data Infra) - 2027 Summer

About the Team Our Search Engineering Team builds and owns TikTok's search engine, combining information retrieval with modern machine learning from NLP, CV, and recommender systems. We embrace a culture of self-direction, intellectual curiosity, openness, and problem-solving. The Data Infrastructure team owns the data layer: how billions of videos and articles are ingested and indexed, how features are computed and served, how petabytes of user actions and features become training data, and how long sequences are modeled. Every retrieval and ranking model in TikTok Search runs on data our systems produce.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

Candidates who pass resume screening will be invited to participate in Our Company's technical online assessment.

Responsibilities - Build and own query/video pipelines — ingestion, enrichment, and streaming/batch index construction at billion-doc scale with near-real-time freshness. - Develop the feature engineering platform: computation, backfill, storage, and serving, with strong online/offline consistency guarantees. - Build streaming and batch sample pipelines that turn user actions and features into high-quality training data. - Improve data quality, observability, and cost efficiency across PB-scale storage and compute.