[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotator-jobs-denver":3},{"Ques":4,"Slug":25,"Header":26,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"No. The role is Remote. The posting targets candidates searching for remote data annotator jobs Denver, but work is performed remotely.","Is this a Denver-only onsite role?",{"A":11,"Q":12},"Common tasks include data labeling for NLP (named entity recognition, classification), computer vision annotation (bounding boxes, segmentation), RLHF preference ranking, prompt evaluation, and QA evaluation for training data quality.","What types of annotation tasks are included?",{"A":14,"Q":15},"RLHF tasks typically involve comparing model responses, ranking outputs by helpfulness and safety, writing short rationales, and following policy-aligned guidelines to support large language model evaluation and model performance improvement.","What does RLHF work look like for a data annotator?",{"A":17,"Q":18},"This listing is FULL_TIME, Remote. Depending on project needs on Rex.zone, some teams may also run contract or freelance tasking in parallel, but this specific job metadata is full-time.","Do you offer contract or freelance options?",{"A":20,"Q":21},"Projects may support AI labs, tech startups, enterprises, BPOs, and annotation vendors across Technology, with datasets spanning NLP, computer vision, content safety labeling, and LLM training pipelines.","Which industries or employer types might the projects support?",{"A":23,"Q":24},"Quality is typically evaluated through audit pass rate, inter-annotator agreement, guideline adherence, calibration results, and error-type trends tied to training data quality and downstream model performance.","How is quality measured?","remote-data-annotator-jobs-denver",{"desc":27,"title":28,"content":29},"Rex.zone is hiring for remote data annotator jobs serving Denver, focused on data labeling and RLHF evaluation that power LLM training pipelines. You will follow annotation guidelines to produce high-quality training data for NLP and computer vision projects, including prompt evaluation, named entity recognition, content safety labeling, and QA evaluation. This role improves model performance through training data quality, annotation consistency, and clear edge-case handling. If you want full-time remote work supporting AI labs, tech startups, and annotation vendors, apply on Rex.zone and help ship reliable AI systems.","Remote Data Annotator Jobs Denver",[30,33,36,39,42,45,48,51,54,57],{"h2":31,"desc":32},"LinkedIn Job Metadata — Remote Data Annotator Jobs Denver","Title: Remote Data Annotator Jobs Denver | 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 Training Pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":34,"desc":35},"About the Role","As a Remote Data Annotator supporting Denver-area hiring needs, you will create and validate labeled datasets used to train and evaluate large language models and computer vision systems. You will apply annotation guidelines compliance, resolve ambiguous examples, and document edge cases to strengthen training data quality and model performance improvement.",{"h2":37,"desc":38},"What You Will Do","Deliver data labeling across NLP and CV tasks such as named entity recognition, text classification, sentiment and intent tagging, image bounding boxes, segmentation, and attribute labeling. Complete RLHF-style ranking and preference labeling, prompt evaluation, and QA evaluation workflows. Run consistency checks, perform peer review, and escalate taxonomy gaps to improve annotation guidelines and reduce disagreement rates.",{"h2":40,"desc":41},"Projects You May Support","LLM training pipelines including instruction following evaluation, safety and policy compliance, hallucination detection, and response quality rating. Computer vision annotation for detection and segmentation datasets. Content safety labeling for abuse, hate, harassment, self-harm, and sensitive content. Enterprise search and NER datasets for product catalogs, healthcare or finance entities (as applicable).",{"h2":43,"desc":44},"Required Qualifications","3+ years in data annotation, data labeling, QA evaluation, or related data operations. Strong understanding of annotation schemas, taxonomy design basics, and inter-annotator agreement concepts. Ability to interpret detailed guidelines, maintain high throughput without sacrificing accuracy, and write clear rationales for RLHF judgments and prompt evaluation decisions.",{"h2":46,"desc":47},"Preferred Qualifications","Experience with RLHF, large language model evaluation, and preference ranking. Familiarity with NLP tasks (NER, classification) and computer vision annotation (bounding boxes, polygons, segmentation). Prior work supporting AI labs, tech startups, BPOs, or annotation vendors. Comfort with content safety labeling and policy-based decisions.",{"h2":49,"desc":50},"Tools and Workflow","You will work in web-based annotation platforms and QA dashboards, following versioned guidelines and calibration sessions. Expect routine auditing, disagreement analysis, and feedback loops designed to raise training data quality and improve model performance. Strong communication is required for documenting edge cases and proposing guideline refinements.",{"h2":52,"desc":53},"Employment Details","Remote Type: Remote. Employment Type: FULL_TIME. This role is listed on Rex.zone for candidates seeking remote, full-time work aligned with Denver searches; projects may include contract or freelance-style tasking within full-time schedules depending on client demand.",{"h2":55,"desc":56},"Compensation","Salary Currency: USD. Salary Min: 63360 per YEAR. Salary Max: 126720 per YEAR. Compensation is based on experience level, task complexity (RLHF, QA evaluation, computer vision annotation), and performance against quality metrics.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone with a resume highlighting data annotation, data labeling, RLHF or prompt evaluation experience, and examples of annotation guidelines compliance. Include any QA evaluation metrics you have owned (accuracy, agreement, audit pass rate) and domains you have labeled (NLP, computer vision, content safety).","AI Data Operations"]