Germany-Based English & German AI Generalist Trainer 2026 May

Rexzone is hiring Germany-based AI Generalist Trainers to support AI/LLM workflows through RLHF-style evaluation, data labeling, and large language model evaluation. You will assess and rank model-generated outputs, write clear rationales, and perform QA evaluation to ensure training data quality, annotation guidelines compliance, and model performance improvement across English and German content.

About the Role

As a Germany-based English & German AI Generalist Trainer at Rexzone, you will help improve model behavior by evaluating, ranking, and validating AI outputs across diverse tasks. Your work directly contributes to training data quality and model performance improvement by applying annotation guidelines compliance, prompt evaluation standards, and content safety labeling practices within large language model evaluation workflows.

Responsibilities

Perform large language model evaluation by assessing model outputs for correctness, helpfulness, and safety; Rank and compare multiple responses using defined rubrics (RLHF-style ranking); Execute QA evaluation on labeled datasets to ensure training data quality and consistency; Write concise, evidence-based reasoning and rationales for evaluation decisions in English and German; Validate edge cases, identify policy violations, and apply content safety labeling where required; Follow annotation guidelines compliance and escalate ambiguities or guideline gaps with clear examples; Conduct prompt evaluation and error analysis to support model performance improvement; Maintain high throughput while preserving attention to detail and repeatable judgments.

Basic Qualifications

Must be based in Germany and authorized to work as a contractor or employee as applicable; Fluent in both German and English (reading, writing, and comprehension); Strong analytical skills with the ability to compare outputs and justify decisions with clear reasoning; Exceptional attention to detail and consistency when following annotation guidelines; Comfortable working in remote, feedback-driven production environments and meeting quality targets.

Preferred Qualifications

Prior experience with AI data labeling, RLHF, LLM evaluation, or similar annotation workflows; Familiarity with large language models, prompt evaluation, and common failure modes (hallucinations, instruction-following errors); Experience with QA evaluation processes, validation sampling, and rubric-based scoring; Self-driven, organized, and able to learn evolving guidelines quickly while maintaining annotation guidelines compliance.

Compensation

USD $35–$40 per hour, depending on experience and assessment performance. This is a full-time, remote role for candidates based in Germany.

How to Apply

Apply through Rexzone with an up-to-date CV highlighting bilingual English/German writing skills, analytical evaluation experience, and any AI/LLM evaluation, data labeling, or QA evaluation background. Qualified candidates may be asked to complete a short skills assessment focused on ranking, validation, and rationale writing.

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 QA evaluation and validation checks, write reasoning-based rationales, follow annotation guidelines compliance, and support training data quality for model performance improvement.
Q: Do I need AI experience?
AI/annotation experience is preferred but not strictly required. Strong analytical skills, attention to detail, and the ability to apply rubrics consistently are essential; training is provided for the workflow and guidelines.
Q: What languages are required?
Fluency in both German and English is required, including strong reading and writing ability for large language model evaluation and rationale writing.
Q: What domains are covered?
You will work across generalist domains such as everyday knowledge, reasoning, summarization, customer-style conversations, and content safety labeling, using RLHF-style ranking and prompt evaluation to improve training data quality.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

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