Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based, bilingual (English/German) AI Generalist Trainers to support RLHF and large language model evaluation by ranking model outputs, performing QA evaluation, and writing clear rationales that drive training data quality and model performance improvement.

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About the Role

As a Germany-based English & German AI Generalist Trainer at Rexzone, you will evaluate, rank, and validate model-generated content across common AI/LLM workflows. Your work supports RLHF, large language model evaluation, and training data quality by applying annotation guidelines compliance, completing prompt evaluation, and documenting reasoning that improves model performance improvement over iterative training cycles.

Responsibilities

• Evaluate and rank model-generated responses against task requirements, safety, and linguistic quality in English and German. • Perform QA evaluation on labeled data, verifying annotation guidelines compliance and correcting inconsistencies. • Write concise rationales explaining reasoning, tradeoffs, and failure modes to support reviewer validation. • Conduct prompt evaluation and comparative ranking for RLHF-style preference data and LLM evaluation. • Validate edge cases, flag ambiguity, and propose guideline clarifications to improve training data quality. • Apply content safety labeling when required, escalating policy-sensitive issues for review. • Track recurring errors, report insights, and help drive model performance improvement through structured feedback.

Basic Qualifications

• Must be based in Germany and able to work remotely within Germany. • Fluency in English and German (reading, writing, and comprehension) with strong grammar and clarity. • Strong analytical skills and comfort with structured evaluation, ranking, and validation. • Excellent attention to detail and consistency when following annotation guidelines. • Ability to explain reasoning clearly and objectively when documenting decisions. • Reliable internet connection and ability to meet quality and throughput expectations.

Preferred Qualifications

• Prior experience with data labeling, content review, QA evaluation, or annotation projects. • Familiarity with LLM evaluation, RLHF concepts, prompt evaluation, or model output ranking. • Experience applying content safety labeling or policy-based evaluations. • Self-driven, organized, and comfortable working independently in a remote environment. • Interest in improving training data quality and contributing to measurable model performance improvement.

Compensation and Schedule

This is a full-time remote role. Compensation is $35–$40 USD per hour, depending on skills and performance during onboarding and ongoing quality reviews.

How to Apply

Apply to Rexzone with a brief summary of your bilingual (English/German) experience, your location in Germany, and any background in evaluation, QA, data labeling, or large language model evaluation. Qualified candidates will be invited to complete an 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 evaluate and rank model outputs, perform QA evaluation and validation checks, complete prompt evaluation, apply annotation guidelines compliance, and write rationales explaining your reasoning.

  • Q: Do I need AI experience?

    AI experience is helpful but not required. Strong analytical skills, attention to detail, and the ability to follow guidelines consistently are essential; training is provided for project-specific workflows such as RLHF and LLM evaluation.

  • Q: What languages are required?

    Fluency in both English and German is required, since you will evaluate and write feedback in both languages.

  • Q: What domains are covered?

    Domains vary by project and can include general knowledge, writing quality, instruction-following, content safety labeling, and large language model evaluation focused on training data quality and model performance improvement.

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

Industry-Leading Compensation

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