Job Description

About the Team

The Platform Responsibility team is bringing disruptive innovation to content safety through the deep application of AI. We are building next-generation infrastructure that leverages advanced LLMs to reshape risk detection and governance. By engineering high-quality data pipelines and integrating AI into core decision-making, we drive exponential efficiency gains and precision. Working at the intersection of scalable engineering and technology, we empower global business growth while serving as the foundation of user trust.

About the Role

Under the spirit of TikTok Community Guidelines, the team leverages the capabilities of LLM and prompt engineering to deliver a large model Agent that can represent "absolute correctness", providing stable evaluation capabilities for TikTok content safety. This role will lead the team to refine strategy operations and prompt optimization to enhance the Agent's precise recall ability for risky content.

Responsibilities:

- Undertake AI special projects for product R&D, perform tasks such as annotation of the LLM model training data sample set, evaluation of model iteration effectiveness, industry application research, and security compliance assurance. Cooperate closely with upstream product technology to jointly promote the achievement of business objectives.

- Standardise Definitions: Responsible for the definition and access criteria of the Golden Set across all risk domains (pornography, violence and terrorism, politics-related, abuse, etc.). Convert qualitative Policy documents into quantitative annotation SOPs executable by the Data Ops team.

- Ground Truth Delivery: Responsible for key aspects of queue delivery, including but not limited to demand analysis, acceptance, solution customization and training, quality assurance, delivery, etc.

- Operation of the Adjudication Mechanism: Establish an adjudication mechanism for internal cases to align consistent judgments on risk issues of different levels.

- Based on an understanding of LLM content safety process design and Community Guidelines, design rule routing logic suitable for LLM execution, and continuously optimize and iterate to improve model performance and resource utilization efficiency.

- Establish an AI content quality management system, be responsible for quality results, and continuously conduct work such as problem diagnosis, data analysis, and model optimization.