Backend Engineer Intern (TikTok Recommendation Architecture, Content Intelligence) - 2026 Start (BS/MS)

Team Introduction Our Content Understanding Architecture Team is responsible for building highly available and scalable content understanding system architectures in areas such as training, inference, and application. Collaborating with the Content Understanding Algorithm Team in areas such as Videos and LIVE, we are committed to efficiently applying large content understanding models(LLMs) in scenarios such as recommendation, security, and push notifications, providing TikTok users with the most stable and optimal experience. We are dedicated to enhancing the stability and reliability of content understanding architecture, continuously improving the performance of the content understanding architecture system through large model(LLMs) training and inference, and creating an industry-leading content understanding architecture.

We are looking for talented individuals to join us for an internship in from Sept. 2025 onwards. Internships at TikTok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok.

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 TikTok and its affiliates' jobs globally.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities

• Participate in content understanding training and inference architecture design to improve the development efficiency, performance, scalability, and stability of the system. - Build a highly available and low-latency content understanding inference architecture to support online and offline training and inference of TikTok's massive content. - Analyze the performance of training and inference architectures, and optimize the efficiency of large model(LLMs) training and inference on GPU-based systems. - Track cutting-edge technologies and applications within the industry, drive the upgrade of the team's technical architecture, and continuously expand application scenarios for content understanding. - Collaborate with colleagues to serve global users and tackle the challenges brought by globalized architecture.