[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-zurich":3},{"Ques":4,"Slug":31,"Header":32,"job_category":68},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They focus on producing high-quality labeled data and evaluations that improve AI\u002FML systems, including RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling.","What are senior data annotator jobs in Zurich focused on?",{"A":11,"Q":12},"Yes. The role is explicitly marked Remote and Employment Type is FULL_TIME, supporting distributed teams and standard performance targets.","Is this role remote and full-time?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) uses human judgments to rank or score model outputs. Your evaluations become training signals that influence model behavior, safety, and helpfulness.","What is RLHF and why does it matter for this job?",{"A":17,"Q":18},"QA evaluation includes auditing labels, checking guideline compliance, reviewing edge cases, measuring agreement, and correcting systematic errors to maintain training data quality.","What does QA evaluation mean in data annotation?",{"A":20,"Q":21},"Common domains include NLP (classification and named entity recognition), large language model evaluation (prompt evaluation and response ranking), computer vision annotation (boxes, polygons, segmentation), and content safety labeling.","Which domains are commonly included?",{"A":23,"Q":24},"Key skills include consistent rubric application, annotation guidelines compliance, strong reasoning and written justification, attention to detail, and experience with LLM evaluation, RLHF, and training data QA processes.","What skills are most important for mid-senior annotators?",{"A":26,"Q":27},"Employers commonly include AI labs, technology companies, startups, BPOs, and specialized annotation vendors running LLM training pipelines and evaluation programs.","What types of employers hire for these roles via Rex.zone?",{"A":29,"Q":30},"This posting is full-time, but the page also reflects common modifiers (contract, freelance, entry-level, senior) used across Rex.zone listings depending on project needs.","Are contract or freelance options available?","senior-data-annotator-jobs-zurich",{"desc":33,"title":34,"content":35},"Senior data annotator jobs in Zurich focus on producing high-accuracy training data for AI\u002FML systems, including LLM training pipelines, RLHF preference ranking, prompt evaluation, and QA evaluation. On Rex.zone, you will apply annotation guidelines compliance to improve training data quality, reduce label noise, and support model performance improvement across NLP, computer vision annotation, and content safety labeling workflows. This remote, full-time role supports teams building large language model evaluation and scalable data labeling operations for AI labs, tech startups, and annotation vendors.","Senior Data Annotator Jobs in Zurich (Remote)",[36,38,41,44,47,50,53,56,59,62,65],{"h2":34,"desc":37},"Title: Senior Data Annotator Jobs in Zurich (Remote)\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, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, large language model training\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","You will perform senior-level data annotation and evaluation to produce reliable datasets for AI training and testing. Your work will span data labeling, RLHF preference judgments, prompt\u002Fresponse evaluation, and quality assurance checks that directly impact large language model evaluation and downstream model behavior.",{"h2":42,"desc":43},"What You Will Do","You will execute tasks across multiple annotation domains, balancing speed with training data quality and consistent guideline adherence.",{"h2":45,"desc":46},"Key Responsibilities","Responsibilities include delivering high-precision labels, running QA evaluation, and mentoring best practices to support model performance improvement across pipelines.",{"h2":48,"desc":49},"Core Workstreams","RLHF and LLM evaluation: rank model outputs, assess helpfulness\u002Fharmlessness, and apply policy-based judgments.\nData labeling and NLP: named entity recognition, intent classification, text span labeling, and prompt evaluation.\nComputer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and visual QA.\nContent safety labeling: classify sensitive content, enforce safety taxonomies, and support trust-and-safety datasets.\nTraining data QA: audits, disagreement analysis, and annotation guidelines compliance checks.",{"h2":51,"desc":52},"Required Qualifications","Mid-senior experience in data annotation or evaluation operations.\nStrong reading comprehension and decision consistency under detailed rubrics.\nDemonstrated ability to follow annotation guidelines compliance and document edge cases.\nComfort working with NLP, LLM training pipelines, and QA evaluation methods.\nAbility to maintain accuracy targets while meeting throughput expectations.",{"h2":54,"desc":55},"Preferred Qualifications","Hands-on experience with RLHF, preference ranking, and large language model evaluation.\nExperience in content safety labeling policy interpretation.\nExperience with computer vision annotation tools and quality sampling.\nFamiliarity with inter-annotator agreement, confusion matrices, and error taxonomy.",{"h2":57,"desc":58},"Tools and Workflow","You will work in web-based labeling platforms, follow versioned guidelines, participate in calibration sessions, and complete periodic gold-task checks. The workflow emphasizes training data quality, auditability, and continuous feedback loops for model performance improvement.",{"h2":60,"desc":61},"Quality and Performance Expectations","You will meet defined targets for accuracy, consistency, and turnaround time.\nYou will pass calibration tasks and maintain stable agreement with team standards.\nYou will provide clear rationales on ambiguous cases and escalate guideline gaps.\nYou will support QA evaluation through sampling, rework, and discrepancy analysis.",{"h2":63,"desc":64},"Why This Role on Rex.zone","Rex.zone connects skilled annotators with remote AI data operations work across AI labs, tech startups, BPOs, and annotation vendors. This position offers exposure to LLM evaluation, RLHF, and multi-domain data labeling while maintaining a remote, full-time setup aligned to global training pipelines.",{"h2":66,"desc":67},"How to Apply","Apply through Rex.zone and be prepared to complete a short skills screening focused on annotation guidelines compliance, training data quality, and evaluation consistency for LLM training pipelines.","AI Data Operations"]