[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-amsterdam":3},{"Ques":4,"Slug":31,"Header":32,"job_category":66},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"A senior data annotator creates high-quality labeled datasets and evaluation judgments used in AI\u002FML training pipelines. This includes data labeling for NLP and computer vision, RLHF preference ranking, prompt evaluation, and QA evaluation to ensure training data quality and improve model performance.","What does a senior data annotator do in AI\u002FML?",{"A":11,"Q":12},"Yes. The role is explicitly marked Remote and the employment type is FULL_TIME.","Is this role remote and full-time?",{"A":14,"Q":15},"The SEO keyword targets Amsterdam-based searches, while the job metadata uses the default Country value (US) per the posting requirements. The work is Remote and supports distributed teams.","Why does the page say Amsterdam if the country is US?",{"A":17,"Q":18},"Common tasks include named entity recognition, text classification, RLHF ranking, rubric-based LLM evaluation, prompt evaluation, content safety labeling, and computer vision annotation such as bounding boxes and segmentation.","What types of annotation tasks are included?",{"A":20,"Q":21},"Quality is measured through audit pass rates, golden set accuracy, annotation guidelines compliance, consistency, rationale clarity, throughput reliability, and the ability to flag systematic dataset issues that affect training data quality.","How is quality measured for senior annotators?",{"A":23,"Q":24},"Highlight data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and experience maintaining annotation guidelines and training data quality.","What skills should I highlight to match these senior data annotator jobs?",{"A":26,"Q":27},"These workflows are used by AI labs, tech startups, annotation vendors, and BPO partners that build datasets for NLP, computer vision, content safety, and large language model evaluation.","What kind of employers use this work?",{"A":29,"Q":30},"This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance opportunities separately, but this page is specifically full-time.","Is this a contract or freelance role?","senior-data-annotator-jobs-amsterdam",{"desc":33,"title":34,"content":35},"Senior data annotator jobs in Amsterdam focus on creating and evaluating training data that powers AI\u002FML systems across NLP, computer vision, content safety, and large language model evaluation. At Rex.zone, you will label data, perform RLHF preference ranking, run prompt evaluation, and execute QA evaluation to improve training data quality, annotation guidelines compliance, and model performance improvement across LLM training pipelines. This remote, full-time role supports AI labs, tech startups, and annotation vendors by producing high-precision datasets for real-world production models while maintaining rigorous quality standards and documentation.","Senior Data Annotator Jobs Amsterdam",[36,39,42,45,48,51,54,57,60,63],{"h2":37,"desc":38},"Job Heading","Keyword: Senior Data Annotator Jobs Amsterdam | Job Title: Senior Data Annotator (Amsterdam)\nDate: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will lead complex data labeling and evaluation tasks that directly impact large language model evaluation and multimodal model readiness. The work includes building high-signal training datasets, executing RLHF ranking and rubric-based scoring, and ensuring annotation guidelines compliance through systematic QA checks. You will collaborate asynchronously with project leads and QA reviewers, document edge cases, and propose guideline updates that reduce ambiguity and increase inter-annotator agreement.",{"h2":43,"desc":44},"What You Will Do","Core responsibilities include: producing high-accuracy annotations for NLP and computer vision datasets; performing RLHF preference ranking and pairwise comparisons; conducting prompt evaluation and response grading for helpfulness, correctness, and safety; executing QA evaluation on sampled batches to measure training data quality; applying named entity recognition, taxonomy tagging, and structured labeling; labeling content safety categories (policy, hate, harassment, self-harm, sexual content) with calibrated severity; writing clear rationales for difficult cases; maintaining versioned notes on guideline interpretations and edge-case decisions; escalating dataset issues (schema drift, label leakage, ambiguous definitions) with actionable recommendations.",{"h2":46,"desc":47},"Domains You May Work In","Projects may include: NLP text classification, NER, summarization evaluation, and instruction-following grading; LLM training pipelines with RLHF and prompt evaluation; computer vision annotation such as bounding boxes, polygons, keypoints, and segmentation masks; multimodal alignment checks; content safety labeling and policy evaluation; search relevance and ranking evaluation; conversational QA evaluation using rubrics and golden sets.",{"h2":49,"desc":50},"Required Qualifications","You should have: experience with data annotation or data labeling in production workflows; strong attention to detail and comfort following complex annotation guidelines; demonstrated ability to perform QA evaluation and explain labeling decisions; familiarity with RLHF concepts, preference data, and evaluation rubrics; ability to work independently in a remote setting with consistent throughput; strong written communication for edge-case documentation; comfort working with ambiguity while maintaining consistency and defensible rationales.",{"h2":52,"desc":53},"Preferred Qualifications","Nice to have: experience with named entity recognition schemas and ontology design; hands-on work with computer vision annotation tools; experience in content safety labeling and policy interpretation; experience creating golden sets, audits, and inter-annotator agreement processes; exposure to prompt engineering workflows, error analysis, and model behavior evaluation; experience supporting AI labs, tech startups, BPOs, or annotation vendors.",{"h2":55,"desc":56},"How Quality Is Measured","Quality standards emphasize: annotation guidelines compliance, low disagreement rates, strong rationale quality, and stable performance on golden tasks. You will be evaluated on training data quality metrics, audit pass rates, consistency over time, turnaround reliability, and the ability to identify systemic issues that impact model performance improvement.",{"h2":58,"desc":59},"Work Arrangement","Remote: This role is explicitly Remote and operates as FULL_TIME. While the keyword targets Amsterdam, the position is hired under US country settings and supports globally distributed teams through Rex.zone workflows.",{"h2":61,"desc":62},"Who This Role Supports","Your labeled datasets and evaluations may support: LLM evaluation teams, safety and trust teams, ranking and retrieval teams, and multimodal model groups. Typical stakeholders include AI labs, tech startups, annotation vendors, and BPO partners building training datasets and evaluation suites.",{"h2":64,"desc":65},"Apply via Rex.zone","Explore and apply for Senior Data Annotator Jobs Amsterdam on Rex.zone. Keep your profile updated with relevant data labeling, RLHF, QA evaluation, NER, computer vision annotation, and content safety labeling experience, and be ready to complete a short skills screening aligned to annotation guidelines and training data quality.","AI Data Operations"]