[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-ai-data-annotation-jobs-detroit":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 Remote and open to US-based candidates, including Detroit. Work is performed online within LLM training pipelines and data labeling workflows.","Is this a remote job even if I live in Detroit?",{"A":11,"Q":12},"AI data annotation is the process of creating and validating labeled datasets used to train and evaluate models. This includes data labeling, QA evaluation, prompt evaluation, RLHF preference ranking, named entity recognition, computer vision annotation, and content safety labeling.","What does AI data annotation mean in this role?",{"A":14,"Q":15},"This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance annotation work separately, but this role remains full-time.","Is the position full-time or contract\u002Ffreelance?",{"A":17,"Q":18},"Projects may include NLP tasks like named entity recognition and classification, computer vision annotation such as bounding boxes and segmentation, and content safety labeling for policy-based evaluation and safer model behavior.","What domains will I work on (NLP, computer vision, content safety)?",{"A":20,"Q":21},"In RLHF, annotators provide preference judgments, rankings, and rubric-based evaluations of model outputs. These labels are used to train reward models and improve model performance improvement for large language model evaluation.","How is RLHF used in data annotation?",{"A":23,"Q":24},"Highlight AI data annotation, data labeling, training data quality, annotation guidelines compliance, QA evaluation, prompt evaluation, RLHF, named entity recognition, computer vision annotation, and content safety labeling.","What skills should I highlight to match this Detroit remote AI data annotation job?",{"A":26,"Q":27},"QA evaluation typically includes calibration tasks, rubric checks, audit sampling, consistency measurement, disagreement resolution, and guideline updates to maintain training data quality across annotators.","What does quality assurance look like for labeling work?",{"A":29,"Q":30},"Rex.zone roles may support AI labs, tech startups, BPOs, and annotation vendors that build and evaluate LLM training pipelines and model evaluation datasets.","Who hires through Rex.zone for this kind of role?","remote-ai-data-annotation-jobs-detroit",{"desc":33,"title":34,"content":35},"Rex.zone is hiring for remote AI data annotation jobs in Detroit, focused on data labeling and large language model evaluation that improves training data quality and model performance improvement. As a search-recognizable Data Annotation Specialist entity, you will apply annotation guidelines compliance across NLP and computer vision annotation, including RLHF, prompt evaluation, QA evaluation, and content safety labeling. Your work supports LLM training pipelines used by AI labs, tech startups, BPOs, and annotation vendors. Explore full-time remote roles and apply through Rex.zone to help build reliable datasets for named entity recognition, classification, and multimodal model training.","Remote AI Data Annotation Jobs in Detroit",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Opening: Remote AI Data Annotation Specialist (Detroit)","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, QA evaluation, prompt 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":40,"desc":41},"About the Role","You will perform remote AI data annotation work supporting end-to-end LLM training pipelines. Tasks include writing and applying labeling instructions, performing QA evaluation against annotation rubrics, running prompt evaluation and response grading for RLHF workflows, and documenting edge cases to improve annotation guidelines compliance. You will label and review datasets spanning NLP (named entity recognition, intent classification, summarization quality) and computer vision annotation (bounding boxes, segmentation, attribute labeling). You will also contribute to content safety labeling and policy-based evaluation to reduce harmful outputs and improve model reliability.",{"h2":43,"desc":44},"What You Will Do","You will label and review text, image, and multimodal data to meet training data quality thresholds; execute RLHF-style ranking and preference judgments for model performance improvement; perform prompt evaluation and rubric-based QA evaluation on LLM responses; annotate NLP tasks such as named entity recognition, taxonomy mapping, and document classification; support computer vision annotation including bounding boxes, polygons, and keypoints; complete content safety labeling across sensitive categories using clear decision rules; maintain annotation guidelines compliance and report ambiguity, drift, and failure modes; collaborate asynchronously with reviewers and project leads to resolve disagreements and improve inter-annotator agreement.",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience in AI data annotation, data labeling operations, or model evaluation; strong understanding of annotation guidelines compliance, training data quality, and QA evaluation processes; ability to follow complex rubrics and provide consistent judgments for RLHF and prompt evaluation; familiarity with NLP concepts such as named entity recognition and text classification; comfort working with computer vision annotation concepts (bounding boxes, segmentation) even if primarily focused on text; strong written communication for documenting edge cases and rationale; reliable remote work habits, including time management and attention to detail.",{"h2":49,"desc":50},"Preferred Qualifications","Prior experience with large language model evaluation, RLHF workflows, or preference ranking; exposure to content safety labeling and policy-based moderation datasets; experience building or improving labeling instructions and calibration sets; familiarity with annotation tooling, audit sampling, and disagreement resolution processes; experience supporting vendors or cross-functional teams (AI labs, tech startups, BPOs, annotation vendors).",{"h2":52,"desc":53},"Work Model, Location, and Schedule","This is a Remote, FULL_TIME role open to candidates in the US, including Detroit. Work is performed online with asynchronous coordination. Some projects may include periodic calibration sessions to align on annotation guidelines compliance and evaluation rubrics.",{"h2":55,"desc":56},"Compensation","Salary Range: 63360 to 126720 USD per YEAR. Compensation may vary by project complexity, evaluation scope (RLHF, QA evaluation, content safety labeling), and demonstrated consistency in training data quality outcomes.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Create or update your Rex.zone profile, highlight AI data annotation and data labeling experience, and emphasize RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling. Submit your application through Rex.zone and be prepared for a short calibration exercise focused on annotation guidelines compliance and training data quality.","AI Data Operations"]