Germany-Based English & German AI Generalist Trainer (Remote, Full-Time) 2026 May

Rexzone is hiring Germany-based bilingual (English/German) AI Generalist Trainers to support large language model evaluation across RLHF workflows. You will evaluate, rank, and QA model outputs, write clear rationales, and follow annotation guidelines compliance to strengthen training data quality and drive model performance improvement.

About the Role

As a Germany-based English & German AI Generalist Trainer at Rexzone, you will contribute to RLHF and large language model evaluation by assessing model-generated responses for helpfulness, accuracy, safety, and instruction-following. Your work directly improves training data quality through consistent application of annotation guidelines compliance, robust QA evaluation, and careful reasoning in written rationales to support model performance improvement.

Key Responsibilities

Evaluate and rank LLM outputs against project rubrics and prompt intent; perform QA evaluation and validation checks to ensure training data quality and consistency; write concise, evidence-based rationales in English and German explaining ranking decisions and reasoning; apply annotation guidelines compliance for data labeling, content safety labeling, and prompt evaluation tasks; identify edge cases, ambiguity, and failure modes to support model performance improvement; maintain documentation and feedback loops with reviewers to resolve guideline questions and calibration issues.

Basic Qualifications

Must be based in Germany and authorized to work as an independent contractor/employee per local requirements; fluent in English and German (C1/C2) with strong writing skills in both; strong analytical skills with the ability to compare outputs, spot contradictions, and justify decisions; excellent attention to detail and consistency when following annotation guidelines compliance; comfortable working remotely with reliable internet and the ability to meet quality and throughput targets.

Preferred Qualifications

Prior experience in AI/annotation work such as RLHF, data labeling, prompt evaluation, QA evaluation, or LLM evaluation; familiarity with how large language models behave, including common hallucination patterns and safety concerns; self-driven, organized, and responsive to feedback during calibration and validation cycles; experience working with multilingual datasets and nuanced German/English language use across domains.

Compensation and Schedule

Pay is USD $35–$40 per hour, depending on assessment results and project scope. Full-time remote capacity is expected, with structured calibration, ongoing QA, and periodic validation reviews.

How to Apply

Apply through Rexzone with an up-to-date CV and a short note describing your bilingual English/German writing experience and any relevant evaluation, QA, or annotation work. Qualified candidates may complete a language and large language model evaluation task as part of the selection process.

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, QA evaluation, validation, prompt evaluation, data labeling, content safety labeling, and writing rationales to improve training data quality and model performance improvement.
Q: Do I need AI experience?
AI experience is helpful but not always required. You must be able to follow annotation guidelines compliance, apply consistent reasoning, and deliver high-quality evaluations; training and calibration are provided.
Q: What languages are required?
Fluency in both English and German is required, including strong written communication in each language.
Q: What domains are covered?
Domains vary by project and can include general knowledge, customer support style tasks, content safety scenarios, and multilingual prompts, all within RLHF and LLM evaluation workflows.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

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