AI Data Annotator, German speakers New
6 days ago
Client: A leading global social networking company
About the Role
As a Generative AI Annotator, you’ll create, assess, and refine question–answer pairs to improve the performance and accuracy of an advanced LLM. You’ll craft realistic prompts, develop thoughtful responses, and ensure outputs meet linguistic and contextual guidelines. Your work will directly shape how AI understands and communicates with people globally.
Key Responsibilities
- Annotate English/German-language data accurately and consistently in accordance with predefined guidelines.
- Evaluate and refine AI-generated outputs and internal datasets.
- Develop and maintain clear, consistent annotation standards.
- Conduct research to ensure accuracy and relevance of annotations.
- Provide feedback and insights to improve processes and data quality.
Candidate Profile
- English proficiency: C1 level or higher (CEFR)
- Fluent in German (oral and written), with a deep understanding of regional culture and social trends.
- Educational background, undergraduate university degree.
- Excellent analytical and research capabilities
- Understanding of social media culture, online communication, and current trends
Compensation
Base salary: €31,063 per year
Bonus: 10% performance-based bonus
Benefits
Private health insurance
Pension contribution (after 12 months of service)
Bike to Work Scheme
Tax Saver Scheme
Relocation
Working Schedule & Location
Working days: 5 days per week Monday–Friday, with occasional weekends and bank holidays.
Shifts: Choose between the Day shift (9 am – 5:30 pm) or Evening shift (6 pm – 2 am) — no rotations.
Training period: 4–5 weeks of training during the Day shift; Evening shift employees transition afterward.
Location: Onsite at the client’s office in Sandyford, Dublin 18, Ireland.
*Due to the sensitive nature of the role, hybrid or remote work is not available
Ready to help shape the future of AI?
Apply now and become part of a global team improving how technology communicates with people — one annotation at a time.
#LI-VR1
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