Software Engineer Intern (TikTok Recommendation Platform) - 2027 Start

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 us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities - Develop intelligent engineering tools and low-code platforms to improve recommendation system R&D efficiency and engineering quality - Build AI Agent-based engineering copilots and workflow automation platforms to automate routine development tasks - Design and implement end-to-end observability systems and intelligent issue attribution mechanisms for global distributed recommendation systems