Italian QA Annotator / Quality Reviewer (C1+ Italian, B2+ English)

About OpenTrain

OpenTrain is a centralized job board for AI training and data-labeling work. We aggregate openings from many AI companies and labeling platforms so you can find annotation and model-evaluation work in one place.

Creating an OpenTrain account is free and applying takes only a few minutes.

• Find curated data-labeling and AI-training projects in one place.
• Free account registration and quick applications.

About AI Training Work

Human reviewers are essential to building safe, accurate AI: people annotate data, evaluate model outputs, and teach models what good responses look like. This role focuses on generative-AI content evaluation and QA for Italian-language text.

Many projects are fully remote, let you choose hours, and require attention to detail more than prior domain-specific credentials.

• Work directly shapes how AI systems respond to users.
• Common perks of the industry: remote work, flexible scheduling, and accessible entry for many contributors.

The Role

You will review and score Italian-language data and AI outputs for quality, correctness, and alignment with detailed guidelines. This is project-based contractor work with flexible hours and less than 20 hours per week.

The position requires living in Italy and strong written skills in Italian and English; a bachelor’s degree or higher is preferred but not required.

• Pay: USD 17.00 per hour (pay-per-hour).
• Type: Contract, part-time; project-based with flexible scheduling.
• Workload: Less than 20 hours per week.
• Subject matter: Generative AI content evaluation and QA on text data.
• Label types: Evaluation ratings and text-generation review.
• Tools: Web-based labeling tools (OTHER).

What You'll Do

Perform precise, guideline-driven reviews of Italian text and model outputs. Your feedback will help improve both labels and the guidelines used by other contributors.

• Audit completed labels and score outputs for quality and correctness.
• Correct errors, classify text, and tag entities and sentiment where required.
• Write concise reviewer notes in Italian with clear, structured feedback.
• Suggest improvements to guidelines and workflows based on recurring issues.
• Occasionally review sensitive or safety-related content and apply rubrics consistently.

Requirements

You must meet all of the listed qualifications to be considered for this role.

• Current residence in Italy (work must be performed from Italy).
• Native or C1 proficiency in Italian.
• English at B2 level or higher.
• At least 1 year of experience in data annotation, labeling, model evaluation, or QA.
• Ability to follow detailed written guidelines and apply consistent rubrics.
• Clear, constructive written feedback skills in Italian.
• Comfortable reviewing potentially sensitive or safety-related content.
• Reliable computer and stable internet to use web-based tools.
• Bachelor’s degree or higher is preferred (but not required).

Who Should Apply

Apply if you have hands-on annotation or QA experience, strong written Italian, and enjoy structured review work that improves AI behavior. This role suits intermediate-level annotators who prefer flexible, part-time contracting.

• Experienced annotators, QA reviewers, or language specialists fluent in Italian.
• People seeking remote, flexible, part-time project work under 20 hours/week.
• Contributors comfortable giving clear, structured feedback and following rubrics.

How It Works

Create a free OpenTrain account to apply. Applications are simple and fast. Selected candidates will work as contractors on a project basis, using web-based labeling tools and detailed guidelines provided by the project.

You will be expected to follow the project’s written instructions closely and record reviewer notes as you audit and rate model outputs.

• Sign up on OpenTrain and submit your application for this listing.
• If selected, you will receive project instructions and use web-based tools to complete reviews.
• Work is paid hourly at USD 17.00 per hour and logged according to the project’s process.