[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-rome":3},{"Ques":4,"Slug":25,"Header":26,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"This posting is explicitly Remote. You can work remotely while targeting the \"senior data annotator jobs Rome\" search intent on Rex.zone.","Are these senior data annotator jobs in Rome remote or on-site?",{"A":11,"Q":12},"Common tasks include RLHF preference ranking, prompt evaluation (helpfulness, factuality, instruction-following), QA evaluation through audits and adjudication, and creating high-quality training data that improves model performance.","What kinds of tasks does a Senior Data Annotator do in LLM projects?",{"A":14,"Q":15},"Both can be relevant. The role may involve named entity recognition and text classification (NLP) as well as computer vision annotation like bounding boxes or segmentation, depending on project needs.","Do I need experience with NLP or computer vision annotation?",{"A":17,"Q":18},"Training data quality refers to label accuracy, consistency, completeness, and low ambiguity. Annotation guidelines compliance means applying definitions and rubrics consistently, documenting edge cases, and following the approved labeling procedure to ensure reliable datasets.","What is meant by training data quality and annotation guidelines compliance?",{"A":20,"Q":21},"This specific role is FULL_TIME and Mid-Senior level. Rex.zone may also list contract, freelance, or entry-level data labeling roles, but those would be separate postings.","Is this role suitable for contract, freelance, or entry-level applicants?",{"A":23,"Q":24},"Senior annotators are commonly hired by AI labs, tech startups, BPOs, and annotation vendors supporting LLM training pipelines, content safety labeling, and model evaluation programs.","What employer types typically hire senior data annotators?","senior-data-annotator-jobs-rome",{"desc":27,"title":28,"content":29},"Senior data annotator jobs in Rome on Rex.zone focus on high-accuracy data labeling and evaluation for AI\u002FML systems. You will produce training data for large language models and computer vision models through RLHF preference ranking, prompt evaluation, QA evaluation, and strict annotation guidelines compliance. This full-time remote role supports LLM training pipelines, content safety labeling, and named entity recognition to drive model performance improvement and training data quality. If you are a mid-senior annotator ready to lead complex labeling workflows and improve dataset reliability, explore this Rex.zone job and apply.","Senior Data Annotator Jobs in Rome (Remote)",[30,33,36,39,42,45,48,51,54],{"h2":31,"desc":32},"Job Heading","Keyword: Senior Data Annotator Jobs Rome — Title: Senior Data Annotator Jobs in Rome (Remote)\nMetadata: 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: Senior data annotation, Data labeling, RLHF evaluation, Prompt evaluation, QA evaluation, Annotation guidelines compliance, Named entity recognition, Computer vision annotation, Content safety labeling, LLM training pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":34,"desc":35},"About the Role","As a Senior Data Annotator, you will own end-to-end labeling and evaluation tasks used to train and validate AI\u002FML models. Your work spans LLM training pipelines (prompt-response evaluation, RLHF preference ranking, and rubric-based scoring) and structured annotation for NLP and computer vision. You will operate at production scale, improve training data quality, mentor reviewers, and collaborate with QA and data operations to reduce defect rates and increase model performance improvement.",{"h2":37,"desc":38},"What You Will Work On","You will label and evaluate complex examples, apply annotation guidelines compliance, and resolve edge cases. You will perform RLHF ranking and pairwise comparisons, prompt evaluation for helpfulness\u002Fharmlessness, QA evaluation via audits and adjudication, and content safety labeling across policy categories. You will also support named entity recognition and taxonomy-driven classification, plus computer vision annotation such as bounding boxes, segmentation masks, and attribute tagging when needed.",{"h2":40,"desc":41},"Key Responsibilities","Own high-complexity annotation queues and deliver consistent, auditable labels. Conduct QA sampling, error analysis, and root-cause reporting for labeling defects. Lead guideline clarifications, create examples, and propose rubric improvements for ambiguity reduction. Perform adjudication and escalation handling for disagreements. Track throughput and quality metrics (accuracy, agreement rate, rework rate) and recommend process improvements aligned to large language model evaluation and training data quality.",{"h2":43,"desc":44},"Required Qualifications","Mid-senior experience in data annotation or data labeling operations with demonstrated QA evaluation rigor. Strong understanding of annotation guidelines compliance and controlled labeling workflows. Familiarity with RLHF evaluation concepts (preference ranking, rubric scoring) and prompt evaluation. Experience with NLP tasks such as named entity recognition and classification, plus comfort with computer vision annotation or willingness to learn. Strong written communication for edge-case documentation and decision rationale.",{"h2":46,"desc":47},"Preferred Qualifications","Experience supporting LLM training pipelines at AI labs, tech startups, BPOs, or annotation vendors. Exposure to content safety labeling and policy-based moderation taxonomies. Experience with inter-annotator agreement methods and calibration sessions. Prior mentorship or lead-review responsibilities for production annotation teams.",{"h2":49,"desc":50},"Tools, Workflow, and Quality Standards","You will work in web-based labeling platforms and follow versioned guidelines with audit trails. Quality expectations include consistent rubric interpretation, reproducible reasoning, and precise application of label definitions. You will participate in calibration, spot checks, and QA evaluation cycles to improve dataset reliability and model performance improvement.",{"h2":52,"desc":53},"Employment Details","Remote Type: Remote. Employment Type: FULL_TIME. Experience Level: Mid-Senior. Industry: Technology. Job Function: Engineering. Compensation range is provided in USD per YEAR. Work is remote while aligned with operational schedules and quality review cadences.",{"h2":55,"desc":56},"How to Apply on Rex.zone","Apply through Rex.zone with a brief summary of your annotation background, domains covered (NLP, computer vision, content safety), and examples of QA evaluation or guideline improvements you led. Include your experience with RLHF or large language model evaluation if applicable.","AI Data Operations"]