[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotation-jobs-washington-dc":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 Remote and open to US candidates, with recruiting focus aligned to Washington DC talent markets.","Is this a remote job even though the keyword mentions Washington DC?",{"A":11,"Q":12},"Senior data annotation typically includes complex labeling, RLHF and prompt evaluation, ownership of QA evaluation routines, guideline interpretation, calibration leadership, and driving training data quality improvements that support model performance improvement.","What does “senior data annotation” mean in practice?",{"A":14,"Q":15},"Projects can include NLP tasks like named entity recognition, LLM evaluation and prompt evaluation, RLHF preference ranking, content safety labeling, and computer vision annotation such as bounding boxes, polygons, or segmentation.","What kinds of projects are included?",{"A":17,"Q":18},"This posting is for FULL_TIME employment. Rex.zone may also list contract and freelance roles separately, but this specific job metadata is Full-Time.","Is this contract or freelance work?",{"A":20,"Q":21},"Quality is measured through QA evaluation methods such as gold sets, blind review, sampling audits, inter-annotator agreement, and error taxonomy tracking tied to training data quality and large language model evaluation results.","How is quality measured?",{"A":23,"Q":24},"Highlight senior data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines.","What skills should I highlight to match the role intent?","senior-data-annotation-jobs-washington-dc",{"desc":27,"title":28,"content":29},"Rex.zone is hiring for senior data annotation jobs supporting real-world AI\u002FML training workflows across Washington DC talent markets. This Mid-Senior, Remote, Full-Time role focuses on data labeling, RLHF evaluation, prompt evaluation, and QA evaluation to improve large language model evaluation and model performance improvement. You will apply annotation guidelines compliance, training data quality controls, and content safety labeling to create reliable datasets for NLP and computer vision annotation programs used by AI labs, tech startups, and annotation vendors.","Senior Data Annotation Jobs in Washington DC (Remote, Full-Time)",[30,33,36,39,42,45,48,51],{"h2":31,"desc":32},"Senior Data Annotation Jobs in Washington DC — Role Overview (LinkedIn Job Metadata)","Title: Senior Data Annotation Specialist (Washington DC)\nDate: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: Senior data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"About the Role","You will lead senior data annotation workstreams for LLM training pipelines, combining high-precision data labeling with RLHF and QA evaluation. You will review annotation guidelines, run calibration sessions, and deliver training data quality improvements that translate into measurable model performance improvement. Projects may include named entity recognition for NLP, prompt evaluation for LLM alignment, computer vision annotation (bounding boxes, polygons, keypoints), and content safety labeling for policy and risk coverage.",{"h2":37,"desc":38},"Key Responsibilities","You will: (1) execute and review complex data annotation tasks across text, image, and multimodal datasets; (2) perform RLHF ranking, preference data collection, and rubric-based prompt evaluation; (3) run QA evaluation workflows, including gold-set creation, inter-annotator agreement checks, and error taxonomy reporting; (4) enforce annotation guidelines compliance through audits and feedback loops; (5) document edge cases and resolve label ambiguity to protect training data quality; (6) collaborate with engineering and research stakeholders to connect labeling outputs to LLM evaluation metrics; (7) support scalable delivery for AI labs, tech startups, BPOs, and annotation vendors via Rex.zone.",{"h2":40,"desc":41},"Required Qualifications","You have experience in senior data annotation or data labeling with demonstrated QA evaluation ownership. You can interpret and apply detailed annotation guidelines, communicate edge cases clearly, and maintain high training data quality under throughput targets. You are comfortable with RLHF concepts, rubric-based prompt evaluation, and large language model evaluation. Familiarity with named entity recognition, taxonomy design, and at least one computer vision annotation format is preferred.",{"h2":43,"desc":44},"Tools and Data Modalities","You will work with common annotation tooling and workflows for: text classification, sequence labeling, named entity recognition, retrieval relevance, prompt-response grading, RLHF preference ranking, and computer vision annotation (bounding boxes, polygons, segmentation masks). You will also use QA evaluation techniques such as gold data, blind review, sampling plans, and disagreement resolution to ensure annotation guidelines compliance.",{"h2":46,"desc":47},"Working Model (Remote) and Location Note","This is a Remote, Full-Time role open to candidates in the US, with hiring focus aligned to Washington DC. Remote roles remain explicitly Remote. You will collaborate asynchronously with cross-functional teams and participate in calibration sessions to maintain consistent rubric interpretation and training data quality.",{"h2":49,"desc":50},"What Success Looks Like","Success means delivering consistent labels that improve large language model evaluation outcomes, reducing rework through strong annotation guidelines compliance, and elevating training data quality via QA evaluation. You will identify systematic error patterns, propose guideline refinements, and help scale reliable RLHF and prompt evaluation processes across multiple projects.",{"h2":52,"desc":53},"How to Apply on Rex.zone","Apply through Rex.zone by submitting your resume and a short summary of data labeling, RLHF, prompt evaluation, and QA evaluation experience. If available, include examples of annotation guideline contributions, calibration leadership, error taxonomies, or training data quality dashboards. Candidates may complete a skills assessment involving named entity recognition, content safety labeling, or LLM evaluation rubrics.","AI Data Operations"]