Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based, bilingual (English/German) AI Generalist Trainers to support large language model evaluation through RLHF-style ranking, prompt evaluation, and training data quality improvements. You will assess model-generated outputs, apply annotation guidelines compliance, perform QA evaluation, and write clear rationales that drive model performance improvement in real-world AI/LLM workflows.

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About the Role

As a Germany-based English & German AI Generalist Trainer at Rexzone, you will evaluate and improve AI systems by reviewing, ranking, and validating model-generated content. Your work directly supports RLHF, large language model evaluation, and training data quality by applying consistent annotation guidelines compliance and structured QA evaluation. This is a remote, full-time role focused on careful reasoning, evidence-based judgments, and clear written rationales in both German and English.

Key Responsibilities

You will: (1) evaluate and rank LLM outputs against rubrics for helpfulness, correctness, safety, and style; (2) perform prompt evaluation and response comparison, including pairwise ranking for RLHF pipelines; (3) write concise, well-structured rationales that justify decisions using explicit criteria and sound reasoning; (4) execute QA evaluation, validation checks, and disagreement resolution to improve training data quality; (5) follow annotation guidelines compliance, flag guideline gaps, and propose clarifications; (6) label content for content safety labeling (e.g., policy, toxicity, self-harm, hate, privacy) and escalate edge cases; (7) track errors, calibrate with the team, and maintain consistent annotation quality across English and German tasks.

Basic Qualifications

You must: (1) be based in Germany and able to work remotely from Germany; (2) be fluent in both German and English (reading and writing at a professional level); (3) demonstrate strong analytical skills for comparing outputs, identifying logical issues, and applying scoring rubrics; (4) have exceptional attention to detail and consistency when following annotation guidelines compliance; (5) be comfortable working with web-based annotation tools and handling high-volume evaluation and ranking tasks.

Preferred Qualifications

Nice to have: (1) prior experience in data labeling, QA evaluation, or annotation workflows; (2) familiarity with LLM evaluation, RLHF concepts, prompt evaluation, and training data quality processes; (3) experience writing structured rationales and performing validation on edge cases; (4) self-driven, reliable, and able to manage productivity in a remote environment; (5) interest in model performance improvement and responsible AI practices, including content safety labeling.

How to Apply

Apply to Rexzone with a short summary of your bilingual (German/English) experience, availability for full-time remote work in Germany, and any background in data labeling, QA evaluation, or large language model evaluation. Highlight examples that show careful reasoning, consistent rubric use, and attention to detail.

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-generated outputs, perform prompt evaluation, complete QA evaluation and validation, apply annotation guidelines compliance, write rationales, and contribute to training data quality for large language model evaluation and RLHF workflows.

  • Q: Do I need AI experience?

    AI experience is helpful but not required. You do need strong analytical skills, attention to detail, and the ability to follow rubrics and annotation guidelines compliance consistently.

  • Q: What languages are required?

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

  • Q: What domains are covered?

    Tasks can span general knowledge, reasoning, writing quality, instruction-following, and content safety labeling, with the goal of improving training data quality and model performance improvement across multiple use cases.

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

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