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 through RLHF, ranking, and QA evaluation workflows that strengthen training data quality and drive model performance improvement.

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About the Role

As a Germany-based English & German AI Generalist Trainer at Rexzone, you will evaluate and improve AI/LLM systems by assessing model outputs, ranking responses, and writing clear rationales. Your work directly supports RLHF pipelines, large language model evaluation, and training data quality initiatives. You will apply annotation guidelines compliance to ensure consistent judgments, validate edge cases, and perform QA evaluation to reduce errors and increase reliability for model performance improvement.

Responsibilities

Perform large language model evaluation by reviewing model-generated outputs in English and German; rank multiple responses using defined rubrics and reasoning standards; write concise rationales that justify selections and enable RLHF learning signals; execute QA evaluation by checking labeled items for correctness, consistency, and annotation guidelines compliance; validate ambiguous cases, identify policy gaps, and escalate issues with evidence; apply prompt evaluation to test instruction-following, helpfulness, and factuality; support content safety labeling where required and document decisions for auditability; maintain training data quality by following workflows, meeting throughput targets, and ensuring high inter-annotator consistency.

Basic Qualifications

Must be based in Germany and eligible to work as a remote contractor/employee per local requirements; full professional fluency in English and German (reading and writing); strong analytical skills with the ability to compare alternatives, detect contradictions, and assess reasoning quality; exceptional attention to detail and ability to follow rubric-based annotation guidelines compliance; comfort working with web-based tooling, spreadsheets, and structured evaluation forms; ability to explain decisions clearly and consistently in written rationales.

Preferred Qualifications

Prior experience in data labeling, RLHF, prompt evaluation, QA evaluation, or content safety labeling; familiarity with LLM behavior, failure modes, and large language model evaluation concepts; experience applying annotation guidelines and improving training data quality through feedback loops; self-driven, reliable, and able to manage time independently in a remote environment; background in linguistics, writing/editing, QA, customer support, research, or technical documentation is a plus.

Compensation and Schedule

Pay rate: USD $35–$40 per hour, depending on performance and project needs. Full-time, remote. Work is task-based within defined quality thresholds, with ongoing feedback to support training data quality and model performance improvement.

How to Apply

Apply through Rexzone with an up-to-date resume/CV highlighting bilingual English/German work, analytical evaluation experience, and any AI, annotation, or QA background. Selected candidates may complete a short skills assessment focused on ranking, reasoning, and annotation guidelines compliance.

Frequently Asked Questions

  • Q: Is this role remote?

    Yes. This is a remote, full-time role, and applicants must be based in Germany.

  • Q: What tasks will I do?

    You will perform large language model evaluation, including evaluation and ranking of model outputs, writing rationales, validation of edge cases, and QA evaluation to maintain training data quality and support RLHF workflows.

  • Q: Do I need AI experience?

    AI experience is preferred but not required. You must be able to follow rubrics, demonstrate strong analytical reasoning, and meet annotation guidelines compliance standards; Rexzone provides project-specific guidance.

  • Q: What languages are required?

    Full professional fluency in both English and German is required, including reading and writing for prompt evaluation and rationale writing.

  • Q: What domains are covered?

    Domains commonly include general knowledge, instruction-following, reasoning quality, factuality checks, and content safety labeling, depending on the project’s large language model evaluation scope.

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

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