[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-phoenix":3},{"Ques":4,"Slug":31,"Header":32,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. The role is explicitly marked Remote while targeting Phoenix-based search intent for senior data annotator jobs.","Are these senior data annotator jobs in Phoenix remote?",{"A":11,"Q":12},"A senior data annotator produces and audits labeled datasets used for model training and evaluation, including data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, and documentation that improves training data quality and downstream model performance.","What does a senior data annotator do in AI\u002FML training pipelines?",{"A":14,"Q":15},"Common tasks include named entity recognition and other NLP labeling, computer vision annotation (e.g., bounding boxes or segmentation), content safety labeling, and LLM evaluation with rubric-based scoring and prompt-response assessment.","What tasks are included (NLP, computer vision, content safety, LLM evaluation)?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) uses human preference judgments or rubric scores to guide model optimization. Senior annotators perform preference ranking, pairwise comparisons, and consistency checks that become training signals for alignment and quality.","What is RLHF and how does it relate to this job?",{"A":20,"Q":21},"Skills should align with senior data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and training data quality.","What skills should match this Phoenix senior data annotator posting?",{"A":23,"Q":24},"Yes. The employment type is FULL_TIME and the experience level is Mid-Senior, with expectations of leading quality, handling ambiguous edge cases, and improving annotation guidelines compliance.","Is this role full-time and what experience level is expected?",{"A":26,"Q":27},"The listed range is 63360 to 126720 USD, paid per YEAR.","What salary range is listed and how is it paid?",{"A":29,"Q":30},"Employers commonly include AI labs, technology companies, tech startups, BPOs, and annotation vendors building and evaluating NLP, computer vision, and LLM systems.","What types of employers hire for this work through Rex.zone?","senior-data-annotator-jobs-phoenix",{"desc":33,"title":34,"content":35},"Senior data annotator jobs in Phoenix focus on producing high-quality labeled data for AI\u002FML training workflows on Rex.zone, including RLHF, data labeling, QA evaluation, and prompt evaluation for large language models. In this remote, full-time role, you will apply annotation guidelines compliance across NLP and computer vision tasks such as named entity recognition, content safety labeling, and LLM evaluation to improve training data quality and model performance improvement. You will collaborate with engineers and AI teams, audit edge cases, resolve ambiguity, and document decisions so datasets remain consistent, reliable, and production-ready for AI labs, tech startups, and annotation vendors.","Senior Data Annotator Jobs in Phoenix",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Openings: Senior Data Annotator Jobs in Phoenix","Keyword + Job Title: senior data annotator jobs Phoenix — Senior Data Annotator Jobs in Phoenix\nMetadata: Title: Senior Data Annotator Jobs in Phoenix | 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, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will lead day-to-day annotation work and quality control for datasets that train and evaluate NLP and LLM systems. The work includes RLHF preference ranking, prompt-response evaluation, fact-checking\u002Fgrounding checks, safety policy labeling, and targeted error analysis. You will interpret ambiguous cases, escalate policy gaps, propose guideline updates, and help ensure consistent labels across annotators and batches. This role is remote while aligned to Phoenix hiring intent and supports production AI pipelines used by AI labs, tech startups, BPOs, and annotation vendors via Rex.zone.",{"h2":43,"desc":44},"What You Will Do","Core responsibilities include: (1) Perform senior-level data labeling across NLP, LLM evaluation, and computer vision annotation tasks; (2) Execute RLHF workflows such as preference ranking, rubric-based scoring, and pairwise comparisons; (3) Conduct QA evaluation, including spot checks, gold-set validation, and inter-annotator agreement reviews; (4) Apply annotation guidelines compliance and document edge-case decisions for repeatability; (5) Run error analysis to identify systematic label issues impacting training data quality; (6) Provide feedback loops to improve taxonomy, rubrics, and evaluation criteria; (7) Partner with engineering and data operations to refine task instructions, sampling, and dataset versioning; (8) Support content safety labeling aligned to policy and risk categories.",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience in data annotation, data labeling, or AI evaluation with demonstrated accuracy and consistency. Strong reading comprehension and attention to detail for rubric-based scoring and policy application. Familiarity with LLM evaluation concepts (helpfulness, correctness, harmlessness), RLHF task formats, and training data quality practices. Ability to follow and improve annotation guidelines compliance, communicate edge cases clearly, and maintain throughput without sacrificing quality. Comfort working remotely with distributed teams and delivering on full-time schedules.",{"h2":49,"desc":50},"Preferred Qualifications","Experience with named entity recognition, intent classification, and prompt evaluation for chat-based systems. Exposure to computer vision annotation such as bounding boxes, polygons, segmentation, and attribute tagging. Prior involvement in content safety labeling, policy taxonomy design, and red-teaming style evaluations. Familiarity with QA metrics like precision\u002Frecall concepts, inter-annotator agreement, and calibration sessions. Experience supporting dataset lifecycle practices such as versioning, audit trails, and release notes.",{"h2":52,"desc":53},"Tools and Workflows","You will use web-based annotation platforms, rubric-driven evaluation forms, and QA workflows (gold sets, consensus review, dispute resolution). Typical workflows include batch labeling, adjudication, calibration, and dataset audit passes. You will track decisions, ensure dataset consistency, and help operationalize improvements that support model performance improvement in downstream training and evaluation pipelines.",{"h2":55,"desc":56},"Compensation and Work Type","This is a Remote, FULL_TIME role in the US market aligned to Phoenix search intent. Salary range is 63360 to 126720 USD per YEAR, depending on evaluation complexity, domain expertise (NLP, computer vision, content safety), and demonstrated QA evaluation leadership.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your resume and a brief summary of relevant annotation experience (RLHF, LLM evaluation, data labeling, QA evaluation). Highlight examples of training data quality improvements, annotation guidelines compliance work, edge-case documentation, and any domain coverage such as named entity recognition, computer vision annotation, or content safety labeling. If available, include prior calibration\u002FQA responsibilities and throughput-quality tradeoff strategies.","AI Data Operations"]