Product Ops Manager: Human Moderation & AI Enablement

TikTok; San Jose (CA); On-site; USD 108,000 - 209,000; Full time

Benefits offered by this job

Health insurance

401(k) savings plan with company match

Paid holidays and paid time off

Job summary

TikTok is seeking a Product Operations Manager (Human Moderation) in San Jose to lead moderation operations within the monetization ecosystem. The role focuses on leveraging data to identify pain points in labeling, QA outcomes, appeals, and risk judgments, and works with cross-functional teams to optimize tools and processes.

The candidate will drive AI-enabled enhancements for rule explanations, risk alerts, and case comparison, while coordinating with Policy, Engineering, and Product teams to

Qualifications

• Strong data analysis skills to diagnose operational pain points and translate findings into improvements.
• Proficiency in English and Chinese to collaborate with Mandarin-speaking partners and stakeholders.
• Excellent cross-cultural understanding, with a results-oriented mindset and ability to influence across teams.

Responsibilities

• Responsible for TikTok monetization ecosystem human moderation operations.
• Use data such as productivity, QA results, appeals, and false negatives to optimize labellers’ pain points.
• Integrate AI capabilities to support moderation, rule explanations, risk alerts, and case comparison to reduce costs.
• Collaborate with Product, Engineering, Algorithm, Policy, QA, Training, and Regional Operations to deliver tool and workflow projects on schedule.
• Develop data-driven insights and thematic reports to align with business direction.
• Explore AI model applications in human review policies and use large language models to support risk assessment.

Skills

Data analysis

Bilingual English/Chinese

Cross-cultural collaboration

Stakeholder influence

Job description

TikTok is seeking a Product Operations Manager (Human Moderation) in San Jose to lead moderation operations within the monetization ecosystem. The role focuses on leveraging data to identify pain points in labeling, QA outcomes, appeals, and risk judgments, and works with cross-functional teams to optimize tools and processes.

The candidate will drive AI-enabled enhancements for rule explanations, risk alerts, and case comparison, while coordinating with Policy, Engineering, and Product teams to