Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based English & German AI Generalist Trainers to support large language model evaluation through RLHF, prompt evaluation, and rigorous training data quality checks. You will evaluate and rank model-generated responses, perform QA evaluation against annotation guidelines, and write clear rationales to drive model performance improvement. This remote, full-time role focuses on data labeling workflows, validation, and content safety labeling to ensure annotation guidelines compliance and consistent, high-quality training signals.

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About the Role

As an AI Generalist Trainer at Rexzone, you will contribute to large language model evaluation by assessing and ranking model outputs in English and German. Your work will directly influence RLHF pipelines and training data quality, helping teams identify failure modes, improve reasoning quality, and drive measurable model performance improvement. You will apply annotation guidelines compliance standards, complete QA evaluation, and provide well-structured rationales that support reliable evaluation and validation.

Key Responsibilities

Evaluate and rank model-generated outputs for correctness, helpfulness, and safety; perform QA evaluation and validation of labeled data to maintain training data quality; write concise, evidence-based rationales that explain reasoning behind rankings; follow annotation guidelines and enforce annotation guidelines compliance across tasks; conduct prompt evaluation, identify edge cases, and flag inconsistencies; support content safety labeling and policy-aligned decisions; collaborate asynchronously, track decisions, and maintain consistent evaluation quality across English and German datasets.

Basic Qualifications

Based in Germany and eligible to work remotely from Germany; fluent in English and German (reading and writing required); strong analytical skills with the ability to compare alternatives and justify rankings; exceptional attention to detail and consistency for high-stakes QA evaluation; comfort working with structured rubrics, validation steps, and tight feedback loops; reliable internet access and ability to work full-time on a remote schedule.

Preferred Qualifications

Experience with data labeling, annotation, or QA evaluation in AI workflows; familiarity with LLM evaluation, RLHF concepts, or prompt evaluation methods; prior work using annotation guidelines and maintaining annotation guidelines compliance; strong writing skills for clear rationales and reasoning; self-driven, organized, and comfortable operating independently in a remote environment.

Compensation

Pay range: $35–$40 USD per hour (hourly).

How to Apply

Apply to Rexzone with a brief summary of your bilingual English/German experience, availability for full-time remote work from Germany, and any background in evaluation, QA, data labeling, or large language model evaluation. Include examples of analytical writing if available.

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 tasks including evaluation, ranking, prompt evaluation, QA evaluation, validation of annotations, and writing rationales to support RLHF and training data quality.

  • Q: Do I need AI experience?

    AI experience is helpful but not required. We value strong analytical skills, attention to detail, and the ability to follow annotation guidelines compliance standards; training will cover task rubrics and workflows.

  • Q: What languages are required?

    Fluency in both English and German is required, including reading and writing.

  • Q: What domains are covered?

    Tasks span general-purpose assistant behavior, reasoning quality, content safety labeling, and multilingual (English/German) evaluation scenarios aligned with RLHF and training data quality goals.

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

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