Backend Engineer Graduate (TikTok Recommendation Architecture) - 2026 Start (BS/MS)
Summary
Build and optimize TikTok’s recommendation architecture, focusing on distributed systems, performance, and scalability to enhance user experience and system stability.
About the Team
Our Recommendation Architecture Team builds and optimizes the recommendation system to provide the most stable experience for TikTok users. The team focuses on architecture, stability, high availability, and performance of both online services and offline data flows, collaborating with the algorithm team to enhance recommendation effectiveness and user experience while reducing costs. We build data and service mid‑platforms and create flexible, scalable high‑performance storage and computing systems.
Responsibilities
- Participate in the design of recommendation systems to enhance development efficiency, performance, scalability, and recommendation effectiveness.
- Optimize backend systems and services for data security, modularity, computational efficiency, and scalability.
- Troubleshoot production systems, and design and implement tools to ensure overall stability.
- Build industry‑leading distributed systems such as storage and computing to provide reliable infrastructure for massive data and large‑scale business systems.
- Achieve extreme performance optimization in the storage layer, computing layer, or application layer.
- Analyse user needs and develop software solutions, applying principles of computer science, engineering, and mathematical analysis.
- Collaborate with colleagues to serve global users and tackle challenges brought by a globalised architecture.
Qualifications
Minimum Qualifications:
- Final‑year graduate with a background in Software Development, Computer Science, Computer Engineering, Electrical Engineering, or related technical discipline.
- Strong software programming capabilities, with good code design and coding style.
- Familiarity with at least one of the programming languages: Go, Python, Java or C++.
- Pragmatic understanding of data structures, algorithm design and analysis, networking, data security, distributed systems, and highly scalable system design.
Preferred Qualifications:
- Interest in recommendation systems, with an understanding of recommendation principles or execution processes.
- Experience or internship in performance optimisation and architecture optimisation in high‑traffic scenarios.
- Agile, quick self‑learner, highly self‑motivated with strong sense of product ownership and a creative problem‑solving attitude.