[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-generalist-trainer-jobs-germany":3},{"Ques":4,"Slug":22,"Header":23,"job_category":45},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19],{"A":8,"Q":9},"Yes. This is a full-time Remote role, and you must be based in Germany.","Is this role remote?",{"A":11,"Q":12},"You will evaluate and rank model outputs, perform QA evaluation and validation, write reasoning-based rationales, follow annotation guidelines compliance, and contribute to training data quality and model performance improvement in large language model evaluation workflows.","What tasks will I do?",{"A":14,"Q":15},"AI experience is helpful but not required. Strong analytical skills, attention to detail, and the ability to follow guidelines and write clear rationales are essential; training is provided for task-specific RLHF and LLM evaluation rubrics.","Do I need AI experience?",{"A":17,"Q":18},"Fluency in both English and German is required, with strong reading and writing skills in both languages.","What languages are required?",{"A":20,"Q":21},"Tasks may cover general knowledge, reasoning, instruction-following, prompt evaluation, and content safety labeling, depending on project needs and the large language model evaluation scope.","What domains are covered?","remote-ai-generalist-trainer-jobs-germany",{"desc":24,"title":25,"content":26},"Rexzone is hiring Germany-based, full-time remote AI Generalist Trainers to support large language model evaluation through RLHF, prompt evaluation, and training data quality workflows. You will evaluate, rank, and QA model outputs in English and German, write clear rationales to enable model performance improvement, and follow annotation guidelines compliance for consistent, high-quality labeling.","Germany-Based English & German AI Generalist Trainer 2026 May",[27,30,33,36,39,42],{"h2":28,"desc":29},"About the Role","As a Germany-based English & German AI Generalist Trainer at Rexzone, you will work in AI\u002FLLM workflows focused on RLHF and large language model evaluation. Your day-to-day work centers on evaluating and ranking model-generated responses, performing QA evaluation and validation checks, and writing reasoning-based rationales that improve training data quality and drive model performance improvement. You will apply annotation guidelines compliance, handle content safety labeling when required, and collaborate with QA and operations to keep labeling consistent across tasks.",{"h2":31,"desc":32},"Key Responsibilities","Perform large language model evaluation by reviewing model-generated outputs for accuracy, helpfulness, and policy adherence; rank and compare responses using defined rubrics (RLHF-style preference ranking); conduct QA evaluation to detect labeling errors, inconsistency, and guideline drift; write concise, evidence-based rationales that explain reasoning behind rankings and decisions; validate edge cases and escalate ambiguous items with clear notes and suggested guideline updates; apply annotation guidelines compliance across English and German tasks, including prompt evaluation and content safety labeling; support training data quality initiatives by sampling, auditing, and improving consistency across datasets; track task metrics and maintain thorough documentation to enable model performance improvement.",{"h2":34,"desc":35},"Basic Qualifications","Must be based in Germany and eligible to work as a remote contractor\u002Femployee as applicable; fluent in English and German (C1\u002FC2 level reading and writing); strong analytical skills with the ability to evaluate nuanced responses and detect subtle errors; exceptional attention to detail and comfort following strict annotation guidelines compliance; ability to produce clear written rationales that reflect sound reasoning and consistent judgment; reliable internet connection and ability to work full-time on a remote schedule.",{"h2":37,"desc":38},"Preferred Qualifications","Prior experience with data labeling, prompt evaluation, or QA evaluation in an annotation environment; familiarity with RLHF concepts, LLM evaluation, and how training data quality impacts model performance improvement; experience working with content safety labeling and policy-based evaluation; self-driven, organized, and comfortable working independently while meeting throughput and quality targets; experience using evaluation tools, spreadsheets, and issue-tracking systems to support validation and auditing.",{"h2":40,"desc":41},"Compensation","USD $35–$40 per hour (paid hourly).",{"h2":43,"desc":44},"How to Apply","Apply to Rexzone with your resume\u002FCV and a short summary of your English\u002FGerman language proficiency and any experience in evaluation, ranking, QA, data labeling, or guideline-driven annotation. Applicants who demonstrate strong reasoning, consistency, and attention to training data quality will be prioritized.","AI Data Operations"]