[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-prague":3},{"Ques":4,"Slug":28,"Header":29,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"A Senior Data Annotator creates and validates labeled training data used to train and evaluate AI models. Typical work includes data labeling, QA evaluation, prompt evaluation for LLMs, and RLHF preference ranking, with a focus on training data quality, annotation guidelines compliance, and reliable ground truth that supports model performance improvement.","What does a Senior Data Annotator do in AI\u002FML workflows?",{"A":11,"Q":12},"They are explicitly marked Remote. Collaboration is distributed, and tasks are completed through online annotation and QA systems while following defined rubrics and annotation guidelines.","Are these Senior Data Annotator jobs Prague roles remote or onsite?",{"A":14,"Q":15},"Common tasks include named entity recognition, text classification, content safety labeling, prompt-response evaluation, RLHF preference ranking, and computer vision annotation such as bounding boxes and segmentation. Many projects also include QA evaluation steps like audits, calibration, and disagreement resolution.","What types of tasks are common for these roles?",{"A":17,"Q":18},"Keyword-aligned skills include Data Annotation, Data Labeling, RLHF, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Evaluation, Annotation Guidelines Compliance, and Training Data Quality.","What skills should match the job intent for Senior Data Annotator jobs Prague?",{"A":20,"Q":21},"Employers include AI labs, technology companies, tech startups, BPOs, and annotation vendors that build and maintain LLM training pipelines and evaluation datasets across NLP, computer vision, and content safety domains.","What employers typically hire for this role type?",{"A":23,"Q":24},"Quality is measured using QA evaluation metrics such as audit accuracy, inter-annotator agreement, rubric adherence, error taxonomies, and consistency over time. Strong performance includes clear rationales, low rework rates, and fast identification of guideline gaps that improve training data quality.","How is quality measured in data annotation?",{"A":26,"Q":27},"Use Rex.zone to find the Remote, FULL_TIME Senior Data Annotator jobs Prague listing and submit your application. Highlight relevant project experience in RLHF, prompt evaluation, content safety labeling, NER, computer vision annotation, and examples of improving training data quality through audits and guideline feedback.","How do I apply via Rex.zone?","senior-data-annotator-jobs-prague",{"desc":30,"title":31,"content":32},"Senior Data Annotator jobs in Prague on Rex.zone focus on producing high-quality training data for AI\u002FML systems through data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation across NLP and computer vision. You will apply annotation guidelines compliance, resolve edge cases, and run training data quality checks that drive model performance improvement for large language model evaluation and production LLM training pipelines. These Remote, FULL_TIME roles support AI labs, tech startups, and annotation vendors building content safety labeling, named entity recognition, and multimodal datasets. Explore and apply on Rex.zone to join distributed teams delivering consistent labels, reliable ground truth, and measurable quality metrics.","Senior Data Annotator Jobs Prague",[33,36,39,42,45,48,51,54,57],{"h2":34,"desc":35},"Senior Data Annotator Jobs Prague — LinkedIn Job Metadata","Title: Senior Data Annotator Jobs Prague | 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, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Evaluation, Annotation Guidelines Compliance, Training Data Quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":37,"desc":38},"About the Role","As a Senior Data Annotator (Remote, FULL_TIME), you will produce and validate labeled datasets used in LLM training pipelines and AI\u002FML model evaluation. Your work spans text, image, and multimodal tasks, including RLHF preference data, prompt-response evaluation, named entity recognition, and computer vision annotation. You will interpret annotation guidelines, surface ambiguous cases, and help maintain training data quality so downstream model performance improvement can be attributed to reliable ground truth.",{"h2":40,"desc":41},"What You Will Do","You will label and review data for NLP tasks (classification, NER, summarization, instruction-following), computer vision tasks (bounding boxes, polygons, keypoints, segmentation), and content safety labeling. You will execute QA evaluation workflows, perform consensus checks, and document decisions for edge cases. You will create RLHF signals via preference ranking, rubric-based scoring, and prompt evaluation to support large language model evaluation. You will track quality metrics, flag guideline gaps, and collaborate with leads to improve annotation guidelines compliance.",{"h2":43,"desc":44},"Core Workstreams (Entity Coverage)","Data annotation and data labeling for supervised learning datasets; RLHF (Reinforcement Learning from Human Feedback) preference ranking; prompt evaluation and rubric scoring; QA evaluation and inter-annotator agreement checks; named entity recognition and taxonomy alignment; computer vision annotation for detection and segmentation; content safety labeling for policy-aligned datasets; LLM evaluation for helpfulness, correctness, and safety within production LLM training pipelines.",{"h2":46,"desc":47},"Requirements","Mid-Senior experience in data annotation, data labeling, or QA evaluation for AI training data. Demonstrated ability to follow annotation guidelines compliance, handle ambiguity, and justify labeling decisions. Familiarity with NLP and\u002For computer vision annotation, including NER schemas and CV bounding\u002Fsegmentation tasks. Experience contributing to training data quality programs (audits, sampling, error taxonomy) and communicating issues clearly in distributed Remote teams.",{"h2":49,"desc":50},"Nice to Have","Experience with RLHF datasets, preference modeling, and prompt evaluation for instruction-tuned LLMs. Background in content safety labeling (policy interpretation, sensitive content handling) and large language model evaluation. Exposure to annotation operations such as calibration sessions, rubric design, and inter-annotator agreement measurement. Prior work with AI labs, tech startups, BPOs, or annotation vendors delivering scaled labeling programs.",{"h2":52,"desc":53},"Quality and Performance Expectations","You will maintain high training data quality through consistent labeling, clear rationales, and strong guideline adherence. You will help reduce disagreement rates by identifying unclear instructions and proposing clarifications. You will support model performance improvement by ensuring labels are accurate, consistent, and aligned to evaluation rubrics used in large language model evaluation. You will participate in QA evaluation loops to detect drift, recurring errors, and dataset bias.",{"h2":55,"desc":56},"Remote Work Notes (Prague Search Intent)","These Senior Data Annotator jobs Prague listings are Remote roles and can support distributed collaboration across time zones. Projects may involve NLP, computer vision annotation, RLHF evaluation, and content safety labeling depending on client needs. You will work full-time with structured workflows, clear rubrics, and measurable quality targets while collaborating with cross-functional partners across engineering and data operations.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your profile and highlighting data labeling, RLHF evaluation, QA evaluation, and guideline compliance experience. Emphasize domains you have supported (NLP, computer vision, content safety) and examples where your annotations improved training data quality or contributed to model performance improvement.","AI Data Operations"]