AI Product Manager Graduate(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.

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 end of 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 TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis. We encourage you to apply as early as possible. Individuals who are completing or have recently completed a Bachelor's/ Master's/ PhD degree in computer science, statistics, information management, data science or a related discipline.

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.