Content Quality and Evaluation Specialist - Safety Model Operations

nt 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.

Qualifications

Minimum Qualifications:

1. Bachelor's degree or above, with preferred experience in internet Trust & Safety, Quality Assurance (QA), AI data evaluation, or policy operations.
2. Exceptional logical reasoning and synthesis skills. Proven ability to navigate complex and ambiguous business scenarios to identify underlying root causes and independently deliver structured solutions.
3. Professional working proficiency in English. Extreme attention to detail and standard consistency, paired with outstanding cross-cultural written and verbal communication skills.
4. Highly self-motivated with a strong sense of ownership. Capable of embracing uncertainty in a fast-paced, cross-border collaborative environment, effectively driving multiple parallel tasks to deliver solid business results.

Preferred Qualifications:

1. Familiarity with machine learning logic and data labeling frameworks is a strong plus.