Polish-Speaking Product Review Data Annotator
About OpenTrain
OpenTrain AI is the organization hiring and contracting for this role. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, helping people discover opportunities to teach and improve artificial intelligence.
- Apply through OpenTrain for this remote, part-time contract opportunity.
- Create an OpenTrain account for free.
About AI Training Work
AI training is the human side of building modern artificial intelligence. People review, classify, and improve examples so AI systems can better understand language, products, and customer feedback.
- Work remotely with a computer and reliable internet connection.
- Build practical experience in data annotation and language-focused AI training.
- Help shape how AI models interpret real-world product reviews.
The Role
OpenTrain AI is seeking native or fluent Polish-speaking Data Annotators for an ongoing product review project. You will review Polish e-commerce product reviews and verify whether product topics and associated sentiment are labeled accurately.
The project focuses on product features and topics such as product quality, along with positive, negative, or neutral sentiment. Experience with data annotation or a similar role is expected, while familiarity with consumer electronics, home goods, or personal care products is a plus.
- Category: General data annotation
- Data type: Text
- Task type: Classification
- Experience level: Intermediate
- Pay: $7 USD per hour
- Employment type: Part-time contractor
- Time requirement: At least 15 hours per week, under 20 hours per week
- Work arrangement: Worldwide and remote
What You'll Do
You will assess Polish product reviews and ensure the correct topics are assigned to the product features mentioned. You will also check whether the sentiment associated with each topic is accurately classified.
- Review Polish-language product reviews.
- Identify and verify topics related to product features.
- Check positive, negative, and neutral sentiment labels.
- Correct inaccurate topic or sentiment assignments.
- Maintain careful, consistent, and accurate annotation decisions.
- Meet project deadlines while working independently.
Requirements
This role is suited to a diligent and reliable annotator who can work independently and maintain strong accuracy across detailed text reviews. You must be able to commit to the project schedule and have the equipment and connectivity needed to complete the work.
- Native or fluent Polish language proficiency.
- Experience in data annotation, data labeling, or a similar field.
- Strong attention to detail and accuracy when reviewing and labeling data.
- Ability to commit at least 15 hours per week on a flexible schedule.
- Reliable internet connection and access to a computer and necessary tools.
- Ability to meet deadlines and work independently.
- Familiarity with e-commerce is a plus, especially consumer electronics, home goods, or personal care products.
Who Should Apply
Apply if you are comfortable interpreting Polish product reviews, distinguishing sentiment, and checking whether labels match the meaning of each review. This opportunity may suit experienced annotators and professionals with relevant e-commerce product familiarity who want flexible, remote work in the growing AI training industry.
- Polish speakers with careful reading and review-analysis skills.
- Annotators who value consistency, reliability, and detail.
- Candidates seeking flexible part-time contract work.
- People interested in helping improve AI-powered product review analysis.
How to Apply
Apply through OpenTrain AI to be considered for this ongoing project. Include your relevant annotation experience, Polish language proficiency, and ability to meet the minimum weekly commitment.
- Review the role details and submit your application through OpenTrain.
- Highlight experience with text classification, sentiment, labeling, or e-commerce reviews.
- Confirm that you can work at least 15 hours per week with a flexible schedule.
