Job Description

About the Team:

Search is no longer just a feature—it is the defining battleground that will determine whether TikTok Shop becomes the first truly global e-commerce super-app. Our Search platform is the core engine behind the "shelf-field" (product card) strategy, and the key to competing head-to-head with Amazon, Taobao, and Pinduoduo in a global market worth over $10 trillion.

Search will propel TikTok Shop from tens of billions to hundreds of billions in annual GMV by activating real shopping intent, unlocking zero-sales products, and ensuring every item becomes instantly discoverable the moment a user types or taps.

We innovate where extreme performance, massive global scale, and uncompromising consistency meet.

As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests.

Applications are reviewed on a rolling basis, so we encourage you to apply early.

Responsibilities:

During this internship, you will join the E-Commerce Engineering team to tackle challenges in distributed systems and information retrieval. You will:

- Core Architecture: Design and develop high-performance, low-latency, and high-availability online search services. Optimize core components including the inverted index, vector retrieval (ANN/HNSW), and query understanding.

- Data Pipelines: Build highly scalable and fault-tolerant data pipelines using Flink, Kafka, and Spark to ensure product changes (price, stock, and new listings) are reflected in search results in near real-time.

- System Optimization: Drive latency and throughput optimizations for services handling hundreds of thousands of QPS. Troubleshoot complex distributed system issues to ensure 99.99% availability.

- Innovation: Participate in the research and implementation of next-generation search infrastructure, focusing on storage decoupling, cloud-native architecture, AI Search, and multi-modal fusion.

- Collaboration: Work with your mentor and ML engineers to productionize state-of-the-art models and build globalized large-scale recommendation systems.

Minimum Qualifications:

- Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, Software Development, or a related technical discipline.

- Must be able to commit to a 12-week full-time work period during Fall 2026.

- Solid programming proficiency in at least one general-purpose language: C++, Go, Java, or Python (C++/Go is heavily preferred for Infra roles).

- Strong foundation in computer science, specifically data structures, algorithms, and system design.

Preferred Qualifications:

- Distributed Systems: Experience with search engines (e.g., Lucene, Elasticsearch, Vespa), distributed storage (RocksDB, Redis), or big data frameworks (Flink, Spark, Kafka).

- Problem Solving: Demonstrated software engineering experience from previous internships, open-source contributions, or competitive programming (e.g., ACM-ICPC).

- Domain Knowledge: Familiarity with information retrieval concepts (BM25, learning-to-rank) or vector similarity search (FAISS, HNSW).