[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotation-jobs-paris":3},{"Ques":4,"Slug":28,"Header":29,"job_category":56},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"They are remote roles aligned with Paris-based job searches where you label and evaluate datasets used to train and validate AI models. Work commonly includes data labeling for NLP and computer vision, RLHF preference ranking, prompt evaluation, and QA evaluation to improve training data quality and model performance.","What are remote data annotation jobs in Paris?",{"A":11,"Q":12},"Yes. Remote Type is Remote and Employment Type is FULL_TIME.","Is this role fully remote and full-time?",{"A":14,"Q":15},"You will perform annotation and evaluation such as named entity recognition, text classification, relevance judgments, LLM rubric scoring, RLHF comparisons, computer vision annotation when needed, and content safety labeling. You will also complete QA evaluation steps like spot checks and disagreement analysis.","What kinds of tasks will I do day to day?",{"A":17,"Q":18},"For this Mid-Senior role, familiarity with RLHF concepts, preference ranking, and large language model evaluation rubrics is expected. Practical experience applying guidelines and resolving edge cases is especially important.","Do I need experience with RLHF or LLM evaluation?",{"A":20,"Q":21},"Emphasize data annotation, data labeling, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. Include examples of how your work improved training data quality or reduced labeling errors.","What skills should I highlight in my application?",{"A":23,"Q":24},"Quality is measured through accuracy audits, guideline compliance checks, inter-annotator agreement signals, error categorization trends, and the clarity of your edge-case documentation that supports consistent labeling at scale.","How is quality measured?",{"A":26,"Q":27},"Apply through Rex.zone by submitting your profile and completing any required calibration tasks that mirror production annotation and LLM evaluation workflows.","Where do I apply?","remote-data-annotation-jobs-paris",{"desc":30,"title":31,"content":32},"Remote data annotation jobs in Paris at Rex.zone support real AI\u002FML training workflows by turning raw text, images, audio, and video into high-quality labeled datasets for large language models and computer vision systems. You will follow annotation guidelines, perform QA evaluation, and contribute to RLHF and prompt evaluation tasks that improve model performance, training data quality, and content safety outcomes. This full-time remote role is designed for Mid-Senior contributors who can deliver consistent labeling accuracy, resolve edge cases, and document decisions for scalable LLM training pipelines across NLP, CV, and safety domains.","Remote Data Annotation Jobs in Paris",[33,35,38,41,44,47,50,53],{"h2":31,"desc":34},"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 annotation, Data labeling, RLHF, LLM evaluation, Prompt evaluation, QA evaluation, Named entity recognition, Computer vision annotation, Content safety labeling, Annotation guidelines compliance | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":36,"desc":37},"About the Role","You will execute end-to-end data labeling and evaluation tasks that directly impact model performance improvement for LLMs and multimodal systems. Work includes training data quality checks, annotation guidelines compliance, adjudication of ambiguous cases, and structured feedback loops used in RLHF, preference ranking, and prompt evaluation. You will collaborate asynchronously with QA, data operations, and model teams to keep labeling consistent across datasets and ensure traceable decisions for audits and iteration.",{"h2":39,"desc":40},"What You Will Do","Deliver high-precision annotations for NLP tasks such as named entity recognition, intent classification, summarization evaluation, and relevance judgments. Perform RLHF-style preference comparisons, rubric-based LLM evaluation, and prompt evaluation focused on helpfulness, correctness, and safety. Annotate computer vision data including bounding boxes, polygons, segmentation masks, and attribute labeling when required. Apply content safety labeling for policy categories (e.g., violence, adult content, self-harm) with consistent taxonomy use. Run QA evaluation routines: spot checks, disagreement analysis, error categorization, and corrective actions. Maintain clear documentation of edge cases, label definitions, and decision rationales to support scalable LLM training pipelines.",{"h2":42,"desc":43},"Requirements","Mid-Senior experience in data annotation, data labeling, or QA evaluation for AI\u002FML datasets. Strong ability to interpret annotation guidelines and apply consistent labeling decisions under ambiguity. Familiarity with RLHF concepts, preference ranking, and evaluation rubrics for large language model evaluation. Practical understanding of NLP and\u002For computer vision annotation workflows, including common error modes. High attention to detail, strong written communication for decision logs, and comfort working remotely with asynchronous feedback cycles.",{"h2":45,"desc":46},"Nice to Have","Experience with content safety labeling taxonomies and calibration sessions. Exposure to prompt evaluation methodologies, red-teaming style checks, or policy-based reviews. Experience improving training data quality through guideline iteration, disagreement resolution, and inter-annotator agreement practices. Familiarity with dataset versioning, audit trails, and structured QA sampling plans.",{"h2":48,"desc":49},"How Success Is Measured","Consistently high annotation accuracy and annotation guidelines compliance. Improved training data quality through reduced error rates and clear edge-case handling. Reliable throughput without sacrificing quality across labeling queues. Strong QA evaluation contributions including actionable error analysis and calibration alignment. Clear documentation that enables reproducible labeling decisions and supports model performance improvement.",{"h2":51,"desc":52},"Work Setup","This is a full-time Remote role. You will work within defined queues and SLAs, participate in calibration and QA review cycles, and collaborate through Rex.zone workflows and tooling to deliver production-ready labeled datasets.",{"h2":54,"desc":55},"Apply on Rex.zone","Explore and apply for remote data annotation jobs in Paris through Rex.zone. Submit your profile, highlight relevant labeling and evaluation experience, and be ready for short guideline-based calibration tasks that reflect real LLM training and QA evaluation workflows.","AI Data Operations"]