Localization And AI Response Evaluator (German)

You will help improve AI systems by creating German prompts and responses, then reviewing model outputs across varied subject areas. The work combines localization judgment with structured evaluation of language, reasoning, accuracy, and cultural fit.

• Create detailed prompts and German responses for AI training.
• Evaluate and rank AI-generated responses for accuracy, fluency, relevance, and adherence to the prompt.
• Review step-by-step reasoning and identify errors in methodology, logic, or concepts.
• Write clear explanations and model solutions that show correct methods.
• Apply German locale conventions, terminology guidance, style rules, and linguistic quality checks.
• Test outputs for inaccurate or unsupported claims, bias, and changes in meaning or certainty.

What It Pays And Takes

This is a fully remote, part-time contractor role. The structured listing calls for at least 20 hours each week.

• Pay: $50 per hour.
• Location: Open to candidates in Austria, Germany, and Switzerland.
• Languages: Fluent German and at least C1 English for prompts, source material, and evaluation guidance.
• Education: Bachelor's degree or higher in German translation, linguistics, localization, communications, or a related field.
• Experience: At least five years of professional localization or translation experience.
• Knowledge of MQM and LQA, including severity, category, and root-cause analysis.
• Proficiency with CAT tools and quality workflows using translation memory, termbases, and automated checks.
• Strong editorial judgment covering register, tone, inclusivity, cultural nuance, and consistency.
• Strong fact-checking, documentation, dependable throughput, and responsiveness while working remotely.
• Previous AI data training, annotation, or evaluation experience is preferred.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that generate and understand language. People write examples, review model responses, and flag errors so AI systems become more accurate, useful, and appropriate across languages and cultures.