Backend Engineer Graduate (TikTok Vertical Recommendation Architecture) - 2027 Start

About the Team Our Recommendation Architecture Team is responsible for building up and optimizing the architecture of the recommendation system for TikTok s vertical businesses, providing the most stable and best experience for TikTok users. We focus on optimizing the recommendation system architecture, ensuring stability and high availability, and improving the performance of both online services and offline data flows. We work closely with algorithm teams to improve recommendation effectiveness while boosting performance and reducing costs., * Participate in the design of the recommendation system to enhance development efficiency, performance, scalability, and recommendation effectiveness.

• Build and maintain high-performance, high-availability online services (retrieval, ranking orchestration, feature and index services) for the TikTok recommendation system.
• Build efficient and reliable data pipelines for candidate generation, profile generation, and training-example generation.
• Pursue extreme performance optimization in the storage layer, computing layer, or application layer; optimize latency (P99), throughput, and resource cost at scale.
• Troubleshoot production issues and build mechanisms that ensure operational stability, including capacity planning, disaster recovery, and monitoring.
• Collaborate with machine learning engineers to productionize models efficiently and safely. Minimum Qualifications

High Availability, Algorithms, Internet/Online Service, Performance Management, System Architecture, Team Player, Cost Control, Onboarding, Candidate Sourcing, Data Management, * Individuals who are completing or have recently completed a Bachelor s degree in Computer Science or a related discipline.

• Strong programming skills with good code design and style; familiarity with at least one of Go, C++, Java, or Python.
• Solid understanding of data structures, algorithms, operating systems, computer networking, and distributed/scalable systems design. Preferred Qualifications
• Interest in recommendation systems, with an understanding of their principles or execution processes.
• Internship experience in performance optimization or architecture work in high-traffic scenarios.
• Familiarity with RPC frameworks, caching, or large-scale storage/messaging systems (Redis, Kafka, etc.).
• Agile and quick learner with self-motivation, a sense of ownership, and creative problem-solving abilities.