[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-labeling-jobs-amsterdam":3},{"Ques":4,"Slug":28,"Header":29,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"Yes. Remote Type is Remote, and the workflows are designed for fully remote delivery with asynchronous collaboration and defined QA evaluation checkpoints.","Are these remote data labeling jobs in Amsterdam fully remote?",{"A":11,"Q":12},"You may work on NLP labeling (named entity recognition, classification, summarization evaluation), computer vision annotation (bounding boxes, segmentation), RLHF preference ranking, prompt evaluation, and content safety labeling depending on project needs.","What kind of data labeling tasks will I work on?",{"A":14,"Q":15},"This posting is for FULL_TIME employment. Rex.zone may also host contract or freelance programs, but this role remains full-time as specified.","Is this role full-time or contract\u002Ffreelance?",{"A":17,"Q":18},"QA evaluation includes review of labeled outputs for consistency, rubric compliance, error categorization, and rework loops that improve training data quality and downstream model performance improvement.","What does QA evaluation mean in this role?",{"A":20,"Q":21},"Mid-Senior candidates are expected to be comfortable with structured evaluation rubrics. Direct RLHF experience is preferred, but strong prompt evaluation and annotation guidelines compliance experience can also be relevant.","Do I need experience with RLHF and LLM evaluation?",{"A":23,"Q":24},"Common domains include NLP, computer vision annotation, content safety labeling, and LLM training pipelines for AI labs, tech startups, and annotation vendors.","Which domains does the role support?",{"A":26,"Q":27},"Include data labeling, data annotation, RLHF, prompt evaluation, QA evaluation, annotation guidelines, named entity recognition, NLP, computer vision annotation, content safety labeling, and LLM training pipelines.","What skills should I include to match this job intent?","remote-data-labeling-jobs-amsterdam",{"desc":30,"title":31,"content":32},"Rex.zone is hiring for remote data labeling jobs in Amsterdam focused on building high-quality training data for AI\u002FML systems. As a Data Labeling Specialist, you will follow annotation guidelines, perform QA evaluation, and support LLM training pipelines through RLHF, prompt evaluation, and content safety labeling. Your work directly improves training data quality, annotation guidelines compliance, and model performance improvement for NLP and computer vision use cases. Explore full-time remote opportunities with clear feedback loops, measurable quality metrics, and production-grade workflows used by AI labs, tech startups, and annotation vendors.","Remote Data Labeling Jobs in Amsterdam",[33,36,39,42,45,48,51],{"h2":34,"desc":35},"Job Overview","Title: Remote Data Labeling Jobs in Amsterdam | Date: 25-02-2026 | Company: Rex.zone | Country: US | Remote Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: data labeling, data annotation, RLHF, prompt evaluation, QA evaluation, annotation guidelines, named entity recognition, NLP, computer vision annotation, content safety labeling, LLM training pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":37,"desc":38},"About the Role","You will deliver production-quality labeled datasets used in large language model evaluation and multimodal model training. This includes interpreting annotation guidelines, labeling text and images, performing second-pass review, and documenting edge cases to improve consistency. Typical workflows include named entity recognition, classification, summarization quality checks, prompt response evaluation, RLHF preference ranking, and content safety labeling. You will collaborate asynchronously with program managers and QA leads, triage ambiguous examples, and help refine rubric definitions to increase inter-annotator agreement and reduce label noise.",{"h2":40,"desc":41},"What You Will Do","Perform data labeling and data annotation across NLP and computer vision annotation tasks (classification, spans, bounding boxes, segmentation, ranking).\nExecute RLHF-style preference judgments and prompt evaluation to support LLM training pipelines and evaluation suites.\nRun QA evaluation checks: consistency audits, spot checks, rework loops, and error taxonomy reporting.\nFollow annotation guidelines compliance requirements and propose clarifications for ambiguous cases.\nMeasure and improve training data quality using agreed metrics (accuracy, agreement, precision\u002Frecall proxies, defect rates).\nLabel and review content safety labeling categories (policy-based safety, toxicity, self-harm, sexual content, violence, regulated goods).\nDocument decisions, edge cases, and rubric updates so future labeling is repeatable and scalable.\nSupport dataset versioning and change management so model performance improvement can be traced to data changes.",{"h2":43,"desc":44},"Required Qualifications","Mid-Senior experience in data labeling, data annotation, QA evaluation, or related data operations roles.\nProven ability to follow annotation guidelines, maintain consistency, and deliver high throughput without quality regression.\nHands-on familiarity with NLP tasks (named entity recognition, intent classification, sentiment, summarization evaluation).\nComfort with computer vision annotation concepts (bounding boxes, polygons, segmentation masks) and review workflows.\nExperience with prompt evaluation and LLM evaluation rubrics (helpfulness, correctness, harmlessness, groundedness).\nStrong written communication for documenting label decisions and contributing to rubric iterations.\nAbility to work full-time in a remote environment with reliable connectivity and disciplined self-management.",{"h2":46,"desc":47},"Preferred Qualifications","Experience with RLHF, preference ranking, or pairwise comparison labeling at scale.\nBackground in content safety labeling, trust & safety operations, or policy-based classification.\nExposure to dataset sampling strategies, disagreement analysis, and error analysis for model performance improvement.\nFamiliarity with annotation tooling, QA dashboards, and structured feedback loops.",{"h2":49,"desc":50},"Work Environment and Collaboration","Remote-first, asynchronous coordination with scheduled quality reviews and calibration sessions.\nClear production expectations, review processes, and escalation paths for ambiguous labeling decisions.\nWork may support AI labs, tech startups, BPOs, and annotation vendors via Rex.zone programs.",{"h2":52,"desc":53},"How to Apply","Apply through Rex.zone with your resume and a brief summary of relevant data labeling and QA evaluation experience.\nHighlight examples of annotation guidelines compliance, training data quality improvements, or LLM evaluation work.\nSelected candidates may complete a short labeling calibration to verify rubric understanding and consistency.","AI Data Operations"]