[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-dublin":3},{"Ques":4,"Slug":25,"Header":26,"job_category":51},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"A Senior Data Annotator produces and reviews labeled data used for model training and evaluation, including data labeling, QA evaluation, prompt evaluation, and RLHF preference judgments. The focus is training data quality, annotation guidelines compliance, and consistent decisions that support model performance improvement in LLM training pipelines.","What does a Senior Data Annotator do in AI\u002FML workflows?",{"A":11,"Q":12},"Yes. Remote Type is Remote and Employment Type is FULL_TIME, aligned with the job metadata listed under the job heading.","Is this role remote and full-time?",{"A":14,"Q":15},"Depending on project assignment, tasks may include NLP labeling such as named entity recognition, computer vision annotation such as boxes\u002Fsegmentation, content safety labeling, and large language model evaluation including RLHF and prompt evaluation.","What types of tasks are included (NLP, CV, content safety)?",{"A":17,"Q":18},"Quality is measured through QA evaluation processes such as audits, calibration rounds, inter-annotator agreement checks, rubric adherence, and error-rate monitoring, all designed to protect training data quality and downstream model performance.","How is quality measured for data annotation work?",{"A":20,"Q":21},"RLHF and LLM evaluation experience is helpful for senior work, but candidates with strong guideline-based labeling, QA review, and structured reasoning can ramp quickly using defined rubrics and calibration sessions.","Do I need experience with RLHF or LLM evaluation?",{"A":23,"Q":24},"Rex.zone listings may include related roles such as Data Labeling Specialist, RLHF Evaluator, Prompt Evaluator, Content Safety Labeler, NLP Annotator, or Computer Vision Annotation QA, across remote, contract, freelance, entry-level, and senior job modifiers.","Where can I find similar roles on Rex.zone?","senior-data-annotator-jobs-dublin",{"desc":27,"title":28,"content":29},"Senior Data Annotator jobs in Dublin on Rex.zone focus on training-data quality for AI\u002FML systems, including data labeling, RLHF, LLM evaluation, and QA review. You will apply annotation guidelines compliance to improve model performance, support large language model evaluation, and strengthen LLM training pipelines across NLP, computer vision annotation, and content safety labeling workflows. Explore this full-time remote role with Rex.zone to help AI labs, tech startups, BPOs, and annotation vendors deliver reliable datasets for prompt evaluation, named entity recognition, and multimodal model training.","Senior Data Annotator Jobs Dublin",[30,33,36,39,42,45,48],{"h2":31,"desc":32},"Job: Senior Data Annotator Jobs Dublin","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, QA evaluation, Prompt 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":34,"desc":35},"About the Role","You will lead end-to-end data annotation and evaluation tasks for AI\u002FML training workflows, producing high-quality labeled datasets and preference judgments that improve model performance. The work includes RLHF ranking, prompt evaluation, QA evaluation, and policy-aligned content safety labeling, while ensuring training data quality and annotation guidelines compliance. You will collaborate with engineers and data operations partners to resolve edge cases, refine rubrics, and maintain consistent labeling standards across NLP, named entity recognition, and computer vision annotation projects.",{"h2":37,"desc":38},"Key Responsibilities","Core responsibilities include: (1) Execute senior-level data labeling and review for text, image, and multimodal datasets, (2) Perform RLHF preference ranking and large language model evaluation using defined rubrics, (3) Run QA evaluation and audits to ensure training data quality, (4) Identify guideline gaps, propose clarifications, and document edge-case decisions, (5) Monitor inter-annotator agreement and resolve disagreements through calibration, (6) Support prompt evaluation for helpfulness, harmlessness, and policy compliance, (7) Contribute to content safety labeling and sensitive-topic handling processes, (8) Partner with engineering and data ops to improve tooling, throughput, and error detection.",{"h2":40,"desc":41},"Required Qualifications","You should have: (1) Experience in data annotation, data labeling, or dataset QA for ML systems, (2) Comfort applying detailed rubrics and maintaining annotation guidelines compliance, (3) Familiarity with RLHF concepts, preference data, or LLM evaluation workflows, (4) Strong written reasoning for judgment-based tasks and edge-case documentation, (5) Ability to sustain high accuracy and consistency under production throughput expectations, (6) Experience with NLP tasks such as named entity recognition or text classification and\u002For computer vision annotation such as bounding boxes, segmentation, or keypoints.",{"h2":43,"desc":44},"Preferred Qualifications","Nice to have: (1) Prior work on content safety labeling, trust and safety, or policy evaluation, (2) Exposure to prompt evaluation, red teaming, or adversarial testing for LLMs, (3) Experience mentoring annotators, running calibrations, or leading QA sampling programs, (4) Familiarity with annotation tools and workflow tracking systems, (5) Understanding of how training data quality connects to model performance improvement and offline\u002Fonline evaluation metrics.",{"h2":46,"desc":47},"Work Modalities and Job Modifiers","This posting targets full-time remote work; similar Rex.zone opportunities may include contract, freelance, entry-level, and senior roles depending on project needs. Projects may span NLP, computer vision annotation, content safety, and LLM training pipelines for employer types such as AI labs, tech startups, BPOs, and annotation vendors.",{"h2":49,"desc":50},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your profile and highlighting relevant annotation experience, RLHF or LLM evaluation exposure, and examples of guideline-driven QA review. Include domains you have labeled (NLP, named entity recognition, computer vision annotation, content safety) and the tooling or processes you have used to maintain training data quality.","AI Data Operations"]