Software Engineer Graduate (TikTok Recommendation Platform) - 2027 Start

Team Introduction: The Recommendation Architecture Team is responsible for designing and developing next-generation recommendation system architectures across multiple products within the company. We ensure system stability and high availability, optimize performance for online services and offline data pipelines, address system bottlenecks, and reduce operational costs. We also abstract reusable system components and platform capabilities to support recommendation and data middleware platforms, accelerate new product incubation, and empower enterprise clients. With the rapid evolution of AI, the team is actively exploring AI Agent-driven engineering systems to reshape R&D workflows, improve development efficiency, and build more intelligent, reliable, and cost-effective engineering infrastructure.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

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 our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities 1. Design and develop intelligent engineering tools for large-scale recommendation systems, providing standardized and productized solutions to improve R&D efficiency and engineering quality. 2. Build AI Agent-based development workflow systems, including long-running agents, autonomous task execution, engineering copilots, and intelligent automation platforms, to improve system stability, operational efficiency, and cost effectiveness across the software development lifecycle. 3. Develop business metrics-driven gray release systems to ensure safe, stable, and efficient release strategies and workflows. 4. Enhance observability for recommendation systems in complex global environments, including multi-region, multi-data center, and multi-language scenarios; establish end-to-end tracing systems and optimize issue attribution mechanisms. 5. Build algorithm engineering toolchains to accelerate the end-to-end process from experimental algorithm/model development to production deployment, improving iteration efficiency and delivery quality. 6. Overhaul platform ecosystem architectures and develop data intelligence assistants to enable smarter system operation, faster problem diagnosis, and more scalable engineering practices.