[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-ai-data-annotation-jobs-austin":3},{"Ques":4,"Slug":25,"Header":26,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. The role is explicitly marked Remote and is open to candidates in the US while aligned to Austin-based hiring intent.","Are these senior AI data annotation jobs in Austin remote?",{"A":11,"Q":12},"Work includes AI data annotation and data labeling across RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling for LLM training pipelines.","What type of annotation work is included?",{"A":14,"Q":15},"This posting is FULL_TIME. Rex.zone may also list contract or freelance annotation roles separately, but this role is full-time.","Is this full-time or contract\u002Ffreelance?",{"A":17,"Q":18},"Senior scope typically includes advanced edge-case handling, guideline refinement, QA evaluation\u002Fauditing, calibration leadership, and measurable training data quality improvements that support model performance improvement.","What does “senior” mean for data annotation?",{"A":20,"Q":21},"Both are supported. Many projects focus on NLP and large language model evaluation, with additional computer vision annotation and multimodal labeling depending on client needs.","Which domains are most common: NLP or computer vision?",{"A":23,"Q":24},"AI labs, tech startups, BPOs, annotation vendors, and technology teams running LLM training and evaluation programs commonly hire for senior AI data annotation roles.","What employers commonly hire for this role type?","senior-ai-data-annotation-jobs-austin",{"desc":27,"title":28,"content":29},"Senior AI data annotation jobs in Austin focus on building high-quality training data for AI\u002FML systems through data labeling, RLHF, prompt evaluation, and QA evaluation. On Rex.zone, you will support large language model evaluation and LLM training pipelines by applying annotation guidelines compliance, auditing training data quality, and improving model performance. This remote, full-time role works across NLP and computer vision annotation, including named entity recognition, content safety labeling, and multimodal tasks. Explore and apply on Rex.zone to help AI labs, tech startups, and annotation vendors ship safer, more reliable models at scale.","Senior AI Data Annotation Jobs in Austin",[30,33,36,39,42,45,48,51],{"h2":31,"desc":32},"Job Heading: Senior AI Data Annotation Jobs in Austin","Title: Senior AI Data Annotation Jobs in Austin | 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: AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":34,"desc":35},"About the Role","You will lead and execute senior-level AI data annotation work for production AI\u002FML training workflows. Projects include RLHF data collection, prompt evaluation, LLM response ranking, QA evaluation for policy compliance, and computer vision annotation. You will translate task specs into clear annotation guidelines, calibrate edge cases with reviewers, and drive training data quality to support model performance improvement.",{"h2":37,"desc":38},"What You’ll Do","Perform expert AI data annotation and data labeling for NLP, LLM training, and computer vision datasets.\nRun RLHF workflows: preference ranking, pairwise comparisons, critique writing, and rubric-based scoring.\nExecute prompt evaluation and response evaluation to identify failure modes, hallucinations, and unsafe outputs.\nApply content safety labeling for toxicity, self-harm, hate, harassment, sexual content, and policy violations.\nConduct QA evaluation and audits to ensure annotation guidelines compliance and consistent labeling decisions.\nCreate and maintain decision logs for ambiguous cases; propose clarifications to improve reviewer alignment.\nMonitor training data quality metrics and sampling strategies that support model performance improvement.\nCollaborate with engineering and AI\u002FML stakeholders to integrate labeled data into LLM training pipelines.",{"h2":40,"desc":41},"Core Workstreams (Entity Coverage)","RLHF (Reinforcement Learning from Human Feedback): preference data, reward modeling signals, rubric design inputs.\nLLM evaluation: prompt evaluation, factuality checks, policy compliance checks, and instruction-following grading.\nNLP labeling: named entity recognition, intent classification, semantic similarity, and conversation tagging.\nComputer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and multi-label attributes.\nContent safety labeling: platform policy taxonomies, risk severity scoring, and safety QA review.",{"h2":43,"desc":44},"Requirements","Mid-Senior experience level with hands-on AI data annotation, data labeling, or evaluation workflows.\nStrong judgment and consistency when applying annotation guidelines to edge cases and nuanced content.\nDemonstrated experience with QA evaluation, error analysis, and training data quality improvement.\nComfort working with LLM outputs, prompt evaluation tasks, and RLHF-style ranking\u002Fscoring frameworks.\nAbility to communicate clearly in written form and maintain accurate documentation for auditability.",{"h2":46,"desc":47},"Nice to Have","Experience with named entity recognition, taxonomy design, and policy-driven content safety labeling.\nExperience with computer vision annotation tools and complex geometries (polygons\u002Fsegmentation).\nExposure to model evaluation concepts (precision\u002Frecall, inter-annotator agreement, calibration exercises).\nBackground collaborating with AI labs, tech startups, BPOs, annotation vendors, or data operations teams.",{"h2":49,"desc":50},"Why This Role on Rex.zone","Remote, full-time work supporting real AI\u002FML training pipelines and LLM training programs.\nOpportunities to contribute across NLP, computer vision, and content safety labeling domains.\nClear operating cadence: guidelines, calibration, QA evaluation, and continuous training data quality improvements.\nWork that directly impacts model reliability, safety, and overall model performance improvement.",{"h2":52,"desc":53},"How to Apply","Visit Rex.zone and open the listing for Senior AI Data Annotation Jobs in Austin.\nHighlight experience in data labeling, RLHF, prompt evaluation, QA evaluation, and guideline compliance.\nShare examples of training data quality work: audits, calibration notes, error analyses, or rubric usage.","AI Data Operations"]