Germany-Based English
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About The Role
In this role, you will evaluate and improve AI/LLM behaviors through RLHF-style workflows. You will review prompts and model responses, validate factuality and instruction-following, apply annotation guidelines compliance standards, and write clear rationales that improve training data quality.
Key Responsibilities
• Perform large language model evaluation across English and German prompts and outputs
• Rank multiple model-generated responses using defined rubrics and consistent reasoning
• Conduct QA evaluation checks by auditing items for annotation guidelines compliance
• Write concise rationales that justify rankings and identify reasoning errors
• Validate outputs for safety, policy adherence, and content safety labeling needs
• Identify edge cases and failure modes; document findings for model performance improvement
• Apply data labeling standards to create reliable feedback signals for RLHF pipelines
• Track discrepancies, escalate guideline questions, and support consistent quality targets Basic Qualifications
• Must be based in Germany and able to work remotely from Germany
• Fluency in English and German (strong reading and writing skills in both)
• Strong analytical skills; ability to evaluate arguments, logic, and evidence
• Exceptional attention to detail and consistency in applying rubrics
• Comfortable working with sensitive or policy-relevant content under safety labeling rules Preferred Qualifications
• Experience with AI data labeling, RLHF, prompt evaluation, or LLM evaluation
• Familiarity with LLM failure patterns (hallucinations, instruction drift, bias, safety issues)
• Background in linguistics, translation, writing/editing, QA, research, or content moderation Compensation Hourly pay range: $30–$50 USD per hour (base), based on skills, language proficiency, and evaluation quality.
How To Apply
Please apply with an updated resume and a brief note describing your English/German proficiency and any experience with evaluation, QA, annotation, or AI/LLM workflows.
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