Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based, bilingual English/German AI Generalist Trainers to support AI/LLM workflows through RLHF, large language model evaluation, and training data quality work that drives model performance improvement. You will evaluate, rank, and QA model-generated outputs, apply annotation guidelines compliance, validate edge cases, and write clear rationales to improve training data quality and model performance improvement across multilingual tasks.

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About the Role

As an AI Generalist Trainer at Rexzone, you will help improve AI systems by performing large language model evaluation and RLHF-style assessments. Your work includes evaluating and ranking model outputs, applying annotation guidelines compliance, completing QA evaluation and validation checks, and documenting reasoning to support model performance improvement. This is a remote, full-time role for candidates based in Germany with fluent English and German.

Responsibilities

Evaluate model-generated responses for correctness, helpfulness, safety, and policy adherence across English and German; rank multiple candidate outputs using defined rubrics to support RLHF; perform QA evaluation by auditing items for annotation guidelines compliance and training data quality; write concise, evidence-based rationales that explain reasoning behind rankings and labels; validate ambiguous cases, resolve edge-case conflicts, and escalate guideline gaps with clear examples; apply content safety labeling where required and ensure consistent decisions across batches; track recurring errors and suggest rubric updates that enable model performance improvement; collaborate asynchronously with project leads to maintain throughput, accuracy, and documentation quality.

Basic Qualifications

Based in Germany and authorized to work from Germany; fluent in English and German (reading and writing at a professional level); strong analytical skills with the ability to compare outputs and justify rankings using clear reasoning; exceptional attention to detail and consistency when applying guidelines; comfort working with web-based labeling/evaluation tools and structured rubrics; ability to follow processes, manage time independently, and meet quality targets.

Preferred Qualifications

Prior experience with data labeling, prompt evaluation, QA evaluation, or content safety labeling; familiarity with LLM evaluation, RLHF concepts, and how training data quality impacts model performance improvement; experience interpreting ambiguous instructions and proposing clarifications; self-driven, reliable, and comfortable working independently in a remote setting; background in linguistics, translation, writing, technical support, or analytical roles that require structured judgment.

Compensation

USD $35–$40 per hour (paid hourly).

How to Apply

Apply to Rexzone with a short summary of your bilingual English/German experience and any relevant evaluation, QA, or annotation work. Selected candidates may complete a brief skills assessment focused on ranking, reasoning, and annotation guidelines compliance.

Frequently Asked Questions

  • Q: Is this role remote?

    Yes. This is a remote, full-time role, and you must be based in Germany.

  • Q: What tasks will I do?

    You will perform large language model evaluation by evaluating and ranking model outputs, running QA evaluation and validation checks, applying annotation guidelines compliance, and writing rationales that support RLHF and training data quality.

  • Q: Do I need AI experience?

    AI experience is helpful but not required. We value analytical skills, attention to detail, and the ability to follow guidelines; we provide role-specific instructions and rubrics.

  • Q: What languages are required?

    Fluent English and German are required, since you will evaluate content and write rationales in both languages.

  • Q: What domains are covered?

    Domains vary by project and can include general knowledge, instruction following, reasoning, multilingual writing quality, content safety labeling, and prompt evaluation focused on model performance improvement.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

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