Global Content Ecosystem Policy Operation 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: - Pay attention to the content ecosystem, balance the relationship between product brands, business development and user experience, lead the sorting and formulation of the framework and details of policy review, deep dive into the issues of existing policies, provide optimization suggestions, and ensure policy rationality and enforceability - Monitor and review key business data indicators, collect feedback from collaborative departments, provide suggestions to improve the systems and policies via summarizing and abstracting issues and cases - Responsible for the training and implementation of policy, support the trainers to effectively train the content reviewer team about platform policy, follow up the training effect and lead the optimizations