AI Product Manager Intern(TikTok-Product-Content Ecosystem)- 2027 Start

Team Introduction: TikTok's Content Ecosystem team is responsible for the health and prosperity of content users and creators to improve DAU in the long term. Our team deeply dives into the following fields: tracking the whole content ecosystem and finding potential challenges and opportunities; cooperating closely with the RD team on model recognition, content understanding, and algorithms, targeting to balance the traffic in different formats, optimize traffic distribution, content diversifications, creator experience of moderation, user experience of interactions, and more problems.

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.

Responsibilities: - Drive content understanding and classification across TikTok’s global ecosystem, including moderation, labeling, and multilingual content comprehension. - Improve the accuracy, scalability, and coverage of AI-powered content detection systems to ensure platform safety. - Collaborate with cross-functional teams (Policy, Trust & Safety, Engineering, Data Science, Operations) to identify gaps and define product solutions. Translate business needs and regional requirements into clear product roadmaps and deliverables. - Build core product capabilities such as multimodal understanding, data pipelines, and annotation tools. - Define evaluation frameworks (e.g., precision, recall) and run experiments to continuously improve model performance.