Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based 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 outputs, write clear rationales, and follow annotation guidelines compliance to strengthen training data quality. This remote, full-time role requires bilingual English/German fluency and careful reasoning, validation, and prompt evaluation across multiple domains while ensuring content safety labeling when needed.

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About the Role

As an AI Generalist Trainer at Rexzone, you will help improve model performance by performing large language model evaluation tasks including RLHF-style ranking, prompt evaluation, and QA evaluation. You will apply annotation guidelines, validate edge cases, and document reasoning to maintain training data quality and consistent annotation guidelines compliance.

Key Responsibilities

Evaluate and rank model-generated responses using RLHF-style preference judgments; perform QA evaluation to validate accuracy, completeness, and policy compliance; write concise, evidence-based rationales that explain reasoning behind rankings; apply annotation guidelines and perform validation checks to ensure training data quality; conduct prompt evaluation and error analysis to support model performance improvement; flag and label sensitive content using content safety labeling standards when required; collaborate asynchronously with leads to resolve guideline ambiguities and improve annotation guidelines compliance.

Basic Qualifications

Based in Germany and authorized to work as a Germany-based remote contractor/employee as applicable; fluent in English and German (reading and writing required for bilingual evaluation); strong analytical skills with the ability to compare outputs and justify rankings with clear reasoning; excellent attention to detail and consistency in QA and validation; comfort working with annotation tools and following detailed annotation guidelines; able to manage quality and throughput in a remote, full-time setting.

Preferred Qualifications

Prior experience in data labeling, LLM evaluation, QA evaluation, or RLHF-related workflows; familiarity with large language model behavior, prompt evaluation, and common failure modes (hallucinations, bias, safety); experience with training data quality processes and calibration/rubric-based ranking; self-driven, reliable, and comfortable operating with ambiguity while maintaining annotation guidelines compliance.

Compensation

USD $35–$40 per hour (hourly).

How to Apply

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

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 (RLHF-style), perform QA evaluation and validation, complete data labeling tasks, write rationales explaining your reasoning, and follow annotation guidelines compliance to protect training data quality.

  • Q: Do I need AI experience?

    AI experience is preferred but not required. Strong analytical skills, attention to detail, and the ability to follow guidelines consistently are required; we provide task instructions and calibration.

  • Q: What languages are required?

    Fluency in both English and German is required for bilingual evaluation and writing.

  • Q: What domains are covered?

    Tasks can span general knowledge, reasoning, writing quality, summarization, instruction following, and content safety labeling, depending on project needs and large language model evaluation requirements.

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

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