Project Epsilon - Chinese Data Trainer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Project Epsilon - Chinese Data Trainer based in United States.

This remote freelance opportunity allows you to contribute to the development and improvement of AI systems through high-quality data validation. You will review image-based questions, analyze visual information, and provide accurate reference answers used to train machine learning models. The role requires strong attention to detail, consistency, and the ability to apply clear guidelines across large task batches. You will work independently while following structured project requirements to ensure reliable and precise data quality. This position is ideal for individuals who enjoy language, technology, and detail-oriented analytical work. By contributing your linguistic expertise and judgment, you will help improve the accuracy and performance of AI-powered solutions.

Accountabilities:: The Project Epsilon - Chinese Data Trainer is responsible for reviewing and validating image-based data, ensuring that AI training datasets meet accuracy and quality standards. The role focuses on careful analysis, consistent application of project guidelines, and delivering reliable outputs within a remote working environment.

Review pre-seeded questions paired with images and determine the correct answers based solely on the visual information provided.

Analyze image content carefully to identify relevant details needed to create accurate “golden” answers.

Provide clear, precise, and consistent responses following project-specific instructions and quality standards.

Identify and flag unclear, incomplete, corrupted, or insufficient images requiring additional review.

Maintain accuracy and consistency across assigned batches of data validation tasks.

Follow detailed guidelines, rubrics, and workflows provided by the project team.

Support the improvement of AI training data through reliable and high-quality annotations.

Requirements:

The ideal candidate is detail-oriented, reliable, and comfortable working independently on structured data review tasks. Strong language skills and the ability to interpret written instructions accurately are essential for success in this role.

Native or strong proficiency in Chinese.

Fluent English proficiency, particularly for reading and written communication.

Bachelor’s degree required.

Exceptional attention to detail and commitment to accuracy.

Ability to follow written instructions precisely and consistently.

Strong analytical skills with the ability to extract relevant information from visual content.

Reliable access to a computer and stable internet connection.

Previous experience in data annotation, data labeling, quality assurance, or content review is an asset.

Benefits:

Fully remote freelance opportunity.

Flexible task-based work format.

Approximately 10 hours per week.

Competitive compensation of $36/hour.

Long-term project opportunity.

Opportunity to contribute to the development of AI technologies.

Work independently while gaining experience in AI data training projects.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1