Content Quality and Evaluation Team Lead - Eco & Social Creation

Responsibilities

About the team
The AI Data Service and Operations (ADSO) team is responsible for providing safety and non-safety data annotation services and search operation services for all of the company's international products, which can also help international products build their own data ecological security.

What will I do?
As the Content Quality & Evaluation Team Lead, you will lead a team of Content Quality and Evaluation Specialist to build the Golden Sets that define Ground Truth for platform policies, AI model evaluation, and global enforcement teams. You will drive deep-dive analyses on high-risk and contentious safety cases, transforming macro policies into crystal-clear SOPs and operational guidelines. By orchestrating a continuous closed-loop feedback system across Operations, Algorithm, Product, and global teams, you will turn Golden Set insights into system-wide improvements-enhancing both human review quality and AI interception accuracy. You will also systematize quality methodologies, establish robust QA mechanisms, and own core data metrics.

Responsibilities
1. Lead High Quality Datasets Production: Lead the team to produce high-quality datasets for model training and evaluation, curating long-tail, high-risk, and complex edge cases to establish accurate Ground Truth for policies, AI models, and global enforcement teams.
2. Drive Deep-Dive & Process Abstraction: Oversee deep-dive analyses on high-risk and highly debated safety cases to identify misapplication patterns and risk trends, translating macro policies into logical SOPs and operational guidelines.
3. Manage Quality Performance: Comprehensively manage quality performance across multiple workflows, drive continuous improvement, and ensure the achievement of both efficiency and quality goals.
4. Lead Cross-Functional Collaboration: Lead collaboration 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.
5. Systematize QA Methodologies: Systematize universal methodologies for the content quality management framework and establish robust daily QA mechanisms, tracking core data metrics to enhance accuracy and operational efficiency.
6. Proactively Identify & Mitigate Risks: Proactively identify quality risks and assess their impact on workflows, agilely resolving risks while ensuring stable delivery.
7. Build Team Capability: Drive team capability building and operational management, including personnel development and resource allocation, to ensure

Qualifications

Minimum Qualifications:

Bachelor's degree or higher with at least 2 years of team management experience.

Deep understanding of content safety policies and guidelines, with excellent content analysis, data analysis, and summarization skills.

Strong English communication skills and logical thinking, with the ability to quickly understand business needs and effectively prioritize tasks.

Strong leadership and operational management skills, familiarity with content quality control tools and management systems, and the ability to effectively motivate teams and handle unexpected situations.

Experience managing cross-market teams or leading project implementation is preferred, with the ability to thrive in a fast-paced, multicultural environment.

Preferred Qualifications:

Proven experience in producing high-quality datasets for model training and evaluation, curating long-tail, high-risk, and complex edge cases to establish accurate Ground Truth for policies, AI models, and global enforcement teams.