Python Code Reviewer (Chinese)
The work
You will review AI-generated Python answers and Chinese-language explanations for accuracy, clear reasoning, and useful problem-solving steps. You will also compare responses, create reference solutions, and write concise feedback for AI training.
The work uses consistent review standards across repeated tasks. You will label common Python errors in both Chinese and English to support reliable evaluation.
- Check Python 3.x solutions for correctness, edge cases, time and space complexity, and idiomatic code.
- Compare multiple model responses and provide concise expert feedback.
- Create clear reference explanations and model solutions.
- Evaluate Chinese technical writing for clarity and precision.
- Apply review rubrics consistently across tasks.
- Annotate common Python error patterns with bilingual labels.
What it pays and takes
This is a part-time, hourly contractor role for someone with strong Python knowledge and the ability to review technical content in Mandarin Chinese. The role is listed as entry level, while prior AI training, annotation, or AI-content evaluation experience is strongly preferred.
- Pay: $27-$50 USD per hour.
- Time: 20+ hours per week.
- Work setup: Remote contractor work.
- Eligible locations: Canada, China, Hong Kong, Singapore, Taiwan, the United Kingdom, and the United States.
- Languages: Fluent Mandarin Chinese and clear written English.
- Python: Strong knowledge of Python 3.x fundamentals, including data structures, functions, modules, and exceptions.
- Experience: Practical debugging and code review experience; prior AI training, annotation, or AI-generated content evaluation is strongly preferred.
- Work style: Detail-oriented, consistent, and self-directed.
How it works
Apply on OpenTrain with your resume and then complete the application on the hiring site.
About AI training work
AI training work uses human review to help models produce better answers. In this role, your Python expertise and language skills help identify errors, judge explanations, and create examples that improve future model responses.
