[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-data-annotation-jobs-phoenix":3},{"Ques":4,"Slug":31,"Header":32,"job_category":63},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They are Remote roles for Phoenix-based candidates focused on producing labeled training data and evaluation signals used in LLM training pipelines, including data labeling, RLHF preference work, prompt evaluation, and QA evaluation for training data quality.","What are remote AI data annotation jobs in Phoenix?",{"A":11,"Q":12},"Common tasks include text classification, named entity recognition, content safety labeling, prompt evaluation, RLHF ranking, and QA checks. Some projects also include computer vision annotation and dataset audits.","What types of tasks will I do day to day?",{"A":14,"Q":15},"For this Mid-Senior role, familiarity with RLHF and large language model evaluation is expected. If you are strong in data labeling and QA evaluation, you may ramp into RLHF workflows through calibration and guided practice.","Do I need experience with RLHF and large language model evaluation?",{"A":17,"Q":18},"Yes. The Employment Type is FULL_TIME and the Remote Type is Remote.","Is this job full-time and fully remote?",{"A":20,"Q":21},"Projects commonly support AI labs, technology companies, tech startups, BPOs, and annotation vendors building datasets for NLP, computer vision, and content safety labeling.","Which industries and employer types does this work support?",{"A":23,"Q":24},"Quality is measured through annotation guidelines compliance, QA evaluation outcomes, audit scores, consensus alignment for RLHF rankings, defect-rate tracking, and improvements tied to training data quality and model performance improvement.","How is quality measured?",{"A":26,"Q":27},"Highlight AI data annotation, data labeling, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines.","What skills should I highlight on my application?",{"A":29,"Q":30},"The salary range is USD 63360 to 126720 per YEAR, depending on scope, complexity, and demonstrated evaluation accuracy.","What is the salary range for this role?","remote-ai-data-annotation-jobs-phoenix",{"desc":33,"title":34,"content":35},"Remote AI Data Annotation Jobs in Phoenix at Rex.zone focus on training-data creation and evaluation for modern AI systems. As an AI data annotation professional, you will label text, image, audio, and video data; follow annotation guidelines compliance; and support large language model evaluation through RLHF, prompt evaluation, and QA review. This role connects directly to LLM training pipelines, model performance improvement, and training data quality for AI labs, tech startups, and annotation vendors. If you are looking for full-time remote work with structured workflows and measurable quality targets, explore Rex.zone opportunities and apply to help build reliable datasets for NLP, computer vision, and content safety labeling.","Remote AI Data Annotation Jobs in Phoenix",[36,39,42,45,48,51,54,57,60],{"h2":37,"desc":38},"Job Heading: Remote AI Data Annotation Jobs in Phoenix","LinkedIn Job Metadata:\n- Title: Remote AI Data Annotation Jobs in Phoenix\n- Date: 25-02-2026\n- Company: Rex.zone\n- Country: US\n- Remote Type: Remote\n- Employment Type: FULL_TIME\n- Experience Level: Mid-Senior\n- Industry: Technology\n- Job Function: Engineering\n- Skills: AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines\n- Salary Currency: USD\n- Salary Min: 63360\n- Salary Max: 126720\n- Pay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will work remotely to produce and evaluate high-quality labeled datasets used in AI\u002FML training. Daily work includes text annotation for NLP tasks (classification, NER, summarization), RLHF ranking and preference labeling, prompt evaluation for instruction-following models, and QA evaluation to enforce training data quality. You will apply detailed labeling rubrics, document edge cases, and contribute feedback that supports model performance improvement across large language model evaluation and content safety labeling.",{"h2":43,"desc":44},"Key Responsibilities","You will:\n- Execute data labeling across NLP, computer vision annotation, and content safety labeling queues.\n- Perform RLHF tasks including pairwise ranking, preference data creation, and rationale capture.\n- Conduct prompt evaluation and response grading for LLM training pipelines.\n- Follow annotation guidelines compliance, track exceptions, and escalate ambiguous cases.\n- Complete QA evaluation (self-checks and peer review) to maintain training data quality.\n- Apply named entity recognition standards and taxonomy rules where required.\n- Meet throughput and accuracy targets while maintaining consistent decision-making.",{"h2":46,"desc":47},"Required Qualifications","You have:\n- Mid-to-senior experience in AI data annotation, data labeling, or QA evaluation workstreams.\n- Familiarity with RLHF, prompt evaluation, and large language model evaluation concepts.\n- Strong reading comprehension and attention to detail for policy-based content safety labeling.\n- Comfort working with annotation tools, guideline documents, and audit workflows.\n- Ability to reason about edge cases, write clear notes, and support calibration sessions.",{"h2":49,"desc":50},"Preferred Qualifications","Nice to have:\n- Experience with named entity recognition projects and schema-driven labeling.\n- Exposure to computer vision annotation (bounding boxes, polygons, keypoints) and image QA.\n- Experience supporting AI labs, tech startups, BPOs, or annotation vendors.\n- Familiarity with dataset sampling, inter-annotator agreement, and error taxonomies.",{"h2":52,"desc":53},"Workflow, Quality, and Metrics","Work is organized in production queues with clear acceptance criteria. You will participate in calibration, apply consistent rubric interpretation, and contribute to training data quality through spot checks and QA evaluation. Typical metrics include guideline adherence, precision\u002Frecall-oriented checks for NER, consensus alignment for RLHF rankings, and defect rate reduction tied to model performance improvement.",{"h2":55,"desc":56},"Remote Work Notes (Phoenix, US)","This is a Remote role for candidates in Phoenix, US. You will collaborate asynchronously with distributed teams, follow secure data handling practices, and maintain reliable availability for QA reviews and calibration sessions.",{"h2":58,"desc":59},"Employment Type and Other Job Modifiers","This posting is FULL_TIME and Remote. Depending on project demand, Rex.zone may also support contract, freelance, entry-level, or senior evaluation tracks in adjacent queues such as NLP, computer vision, and content safety labeling.",{"h2":61,"desc":62},"How to Apply","Apply via Rex.zone and be prepared to complete a short skills screening covering annotation guidelines compliance, QA evaluation, and (where applicable) RLHF or prompt evaluation tasks.","AI Data Operations"]