Job Description

About the team

The Safety Model Operations [SMO] team is responsible for building, optimizing, and maintaining machine learning models and operational processes that support TikTok's Trust & Safety systems. We ensure that automated safety models perform effectively in identifying harmful content, mitigating risks, and maintaining a safe user environment across regions.

The SMO Delivery Team plays a critical role in the organization. Its primary responsibility is to carry out the full spectrum of quality assurance activities for each project. This includes:

- Conducting detailed reviews and complex RCA's to ensure labeling accuracy and consistency

- Monitoring quality performance and compliance against project-specific KPIs

- Identifying trends, risks, and potential gaps in processes or guidelines

- Providing structured feedback and improvement recommendations to the Central Project Team

- Supporting continual optimization of workflows, tools, and evaluation methodologies

- Improve Model performance of AI models

Responsibilities

1. Construct and iterate the core Golden Sets for the content safety ecosystem. Curate long-tail, high-risk, and complex edge cases to establish highly accurate data standards (Ground Truth) for platform safety policies, AI model evaluation, and global enforcement teams.

2. Conduct deep-dive analyses on high-risk and highly debated safety cases to accurately identify misapplication patterns and risk evolution trends. Lead the synthesis and abstraction of complex rules, translating macro policies into highly logical and executable Standard Operating Procedures (SOPs) and operational guidelines.

3. Drive the content safety data closed-loop. Collaborate cross-functionally with Operations, Algorithm, Product, and global teams to identify system vulnerabilities based on Golden Set metrics, achieving bidirectional improvements in human review quality and AI agent interception efficacy.

4. Systematize universal methodologies for the content quality management framework and establish robust daily Quality Assurance (QA) mechanisms. Track and attribute core data metrics to enhance overall data accuracy and operational efficiency.