Team Introduction The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions. We are looking for talented individuals to join us for an internship in from Sept. 2025 onwards. Internships at TikTok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok. 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. Successful candidates must be able to commit to at least 3 months long internship period. Responsibilities - Design and implement real-time and offline data architecture for large-scale recommendation systems. - Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference. - Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features. - Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.
Minimum Qualifications: - Undergraduate, or Postgraduate who is currently pursuing a degree/master in Computer Science, Computer Engineering, Information Systems or a related technical major. - Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure. - Solid understanding of Apache Flink internals, with hands-on experience in state management, connectors, or UDFs. - Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation. Preferred Qualifications: - Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing. - Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training. - Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading. - Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability. - Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus. - (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy If you have any questions, please reach out to us at apac-earlycareers@tiktok.com
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