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

Rexzone is hiring Germany-based English & German AI Generalist Trainers to support large language model evaluation and model performance improvement through RLHF-style ranking, prompt evaluation, and training data quality workflows. You will assess model-generated outputs, write clear rationales, and perform QA evaluation to ensure annotation guidelines compliance. This remote, full-time role requires bilingual German and English fluency and strong attention to detail to validate outputs, improve training data quality, and drive measurable model performance improvement.

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About the Role

As an AI Generalist Trainer at Rexzone, you will evaluate and rank LLM outputs across varied tasks, produce well-reasoned justifications, and help enforce consistent labeling decisions. Your work directly impacts training data quality, annotation guidelines compliance, and large language model evaluation outcomes for safer, more useful AI systems.

Responsibilities

Perform large language model evaluation by reviewing model outputs for correctness, helpfulness, and safety; Rank and compare responses using RLHF-style preference ranking and prompt evaluation criteria; Conduct QA evaluation by validating labels, auditing edge cases, and documenting issues; Write concise, evidence-based rationales that explain reasoning and support consistent judgments; Apply annotation guidelines compliance checks and escalate ambiguous cases with clear recommendations; Validate multilingual (German/English) content for fluency, intent preservation, and policy alignment; Support training data quality initiatives by identifying systematic errors and proposing guideline clarifications; Maintain high throughput while meeting accuracy targets and quality standards.

Basic Qualifications

Based in Germany and authorized to work from Germany; Fluent in German and English (professional reading and writing); Strong analytical skills with the ability to evaluate nuanced content and follow rubrics; Excellent attention to detail and consistency across repeated evaluations; Comfortable working with web tools and structured annotation platforms; Ability to explain reasoning clearly and apply rules consistently for validation and QA.

Preferred Qualifications

Prior experience with data labeling, RLHF, LLM evaluation, or content safety labeling; Familiarity with prompt evaluation, preference ranking, and quality sampling methods; Experience working with annotation guidelines and improving guideline clarity over time; Self-driven, reliable, and able to manage time effectively in a remote environment; Interest in AI reliability, safety, and model performance improvement.

Pay and Schedule

Compensation is USD $35–$40 per hour (based on skills and task complexity). This is a remote, full-time role supporting ongoing evaluation, ranking, QA, reasoning, and validation workflows.

How to Apply

Apply through Rexzone by submitting your resume/CV and a brief note confirming you are Germany-based and fluent in both German and English. Selected candidates may complete a short skills assessment covering rubric-based evaluation, reasoning quality, and annotation guidelines compliance.

Frequently Asked Questions

  • Q: Is this role remote?

    Yes. This is a remote role, and you must be based in Germany.

  • Q: What tasks will I do?

    You will perform large language model evaluation, rank model-generated outputs (RLHF-style), write rationales explaining your reasoning, run QA evaluation checks, and validate labeling decisions for training data quality.

  • Q: Do I need AI experience?

    AI experience is helpful but not strictly required. Strong analytical skills, attention to detail, and the ability to follow annotation guidelines compliance requirements are essential.

  • Q: What languages are required?

    Fluency in both German and English is required, including professional reading and writing in both languages.

  • Q: What domains are covered?

    Domains can include general knowledge, customer support-style prompts, summarization, reasoning tasks, and content safety labeling scenarios, with a focus on model performance improvement and training data quality.

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

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