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Data Annotator

MyRemoteTeam Inc

HybridHybrid

Original Job Summary

Data Annotator

We’re expanding our AI data-labeling projects and are looking for Quality Assurance specialists to review, verify, and ensure accuracy in AI training datasets. Note: This role is specifically for AI Data Annotation QA, not Software QA.

Role

Data Labeling QA (Quality Assurance)

Language Requirement

Native German and Native Russian - candidates must be living outside Russia.

What You’ll Do

  • Carefully review provided data (text, images, or videos).
  • Verify and evaluate search result annotations and AI-generated outputs.
  • Label or classify content based on detailed project guidelines.
  • Identify and flag factually incorrect, sensitive, inappropriate, or unclear content.
  • Ensure accuracy, consistency, and adherence to project standards.
  • Provide clear feedback when necessary.

Requirements

  • Fluency in Russian and German.
  • Prior experience with AI companies/platforms (e.g., Outlier, Appen, Clickworker).
  • Strong logical thinking, fact-checking, and reasoning skills.
  • Excellent attention to detail and ability to follow complex instructions.
  • Strong communication skills and proactivity in asking clarifying questions.
  • A genuine interest in technology and artificial intelligence.

How to Apply

Interested candidates can send their resume (with language proficiency mentioned) to the provided email or contact via WhatsApp.

Email: sajid.ahmed@truelancer.com

WhatsApp: +91-9064877846

Key skills/competency

  • Data Annotation
  • Quality Assurance
  • AI
  • Verification
  • Review
  • Labeling
  • Attention to Detail
  • Logical Thinking
  • Communication
  • Feedback

How to Get Hired at MyRemoteTeam Inc

🎯 Tips for Getting Hired

  • Research MyRemoteTeam Inc: Understand company values and projects.
  • Tailor your resume: Highlight data annotation experience.
  • Show language skills: Emphasize native German and Russian.
  • Prepare examples: Demonstrate detail in QA tasks.

📝 Interview Preparation Advice

Technical Preparation

Practice dataset review exercises.
Learn AI labeling platform functionalities.
Study project guidelines thoroughly.
Run mock data classification tests.

Behavioral Questions

Explain times you provided precise feedback.
Describe prioritizing data verification tasks.
Share examples of clear communication.
Discuss handling ambiguous instructions.