[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-boston":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. Remote Type is Remote. The role targets Boston-area talent for alignment with common schedules and hiring intent, but work is performed remotely within the US.","Is this a remote job even though it targets Boston?",{"A":11,"Q":12},"It means owning training data quality beyond basic labeling: leading QA evaluation, running calibration, enforcing annotation guidelines compliance, and resolving edge cases that impact LLM training pipelines and model performance improvement.","What does “senior data labeling” mean in this role?",{"A":14,"Q":15},"You may work on RLHF-style preference ranking, prompt evaluation, named entity recognition, content safety labeling, and (as needed) computer vision annotation. The mix depends on the active AI\u002FML program on Rex.zone.","What kinds of tasks will I label or evaluate?",{"A":17,"Q":18},"This posting is FULL_TIME. Rex.zone may also host contract or freelance roles, but this specific job is full-time remote.","Is this full-time or contract\u002Ffreelance?",{"A":20,"Q":21},"Demonstrated training data quality ownership, strong QA evaluation habits, experience with RLHF or LLM evaluation, clear documentation for annotation guidelines compliance, and consistent judgment on ambiguous examples.","What skills matter most to get hired?",{"A":23,"Q":24},"Quality is typically measured through accuracy on gold sets, inter-annotator agreement, targeted audits, calibration results, and the downstream impact on model evaluation metrics tied to model performance improvement.","How is quality measured?","senior-data-labeling-jobs-boston",{"desc":27,"title":28,"content":29},"Senior data labeling professionals in Boston will lead training data quality for AI\u002FML systems on Rex.zone, combining data labeling, RLHF evaluation, prompt evaluation, and QA to improve large language model performance. This remote, full-time role supports end-to-end LLM training pipelines: writing and enforcing annotation guidelines, performing named entity recognition and content safety labeling, reviewing complex edge cases, and partnering with engineering to measure model performance improvement. You will own annotation guidelines compliance, calibration, and audit-ready quality workflows across NLP and computer vision annotation tasks while helping scale annotation operations for high-impact production models at Rex.zone.","Senior Data Labeling Jobs in Boston (Remote, Full-Time)",[30,33,36,39,42,45,48,51],{"h2":31,"desc":32},"Job Overview","Title: Senior Data Labeling Specialist (Boston)\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: data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"About Rex.zone","Rex.zone connects experienced labelers and evaluators with AI teams building and improving production models. Our workflows focus on training data quality, annotation guidelines compliance, and measurable model performance improvement across LLM evaluation, NLP labeling, computer vision annotation, and content safety labeling programs.",{"h2":37,"desc":38},"What You Will Do","You will lead high-precision labeling and evaluation work that directly influences model quality and safety. Core responsibilities include:\n- Lead complex data labeling and QA evaluation for LLM training pipelines\n- Execute RLHF-style preference ranking and rubric-based prompt evaluation\n- Create, refine, and enforce annotation guidelines; drive annotator calibration\n- Perform named entity recognition and taxonomy-based labeling for NLP datasets\n- Support computer vision annotation workflows (bounding boxes, polygons, keypoints) as needed\n- Run quality audits, disagreement analysis, and error taxonomy reporting to improve training data quality\n- Flag ambiguous edge cases and collaborate with engineering and ops on policy updates\n- Maintain documentation for annotation guidelines compliance and audit readiness\n- Track quality metrics (accuracy, inter-annotator agreement) and recommend process changes",{"h2":40,"desc":41},"Required Qualifications","To succeed in senior data labeling jobs in Boston (remote), you should have:\n- Experience leading or mentoring labelers in production annotation programs\n- Strong understanding of training data quality, quality assurance, and evaluation rubrics\n- Hands-on experience with RLHF-style ranking, LLM evaluation, or prompt evaluation\n- Familiarity with named entity recognition and structured labeling schemas\n- Comfort interpreting ambiguous language and applying policy consistently\n- Ability to document decisions clearly and uphold annotation guidelines compliance\n- Excellent attention to detail and ability to deliver high accuracy at scale",{"h2":43,"desc":44},"Preferred Qualifications","Nice-to-have experience includes:\n- Exposure to content safety labeling (toxicity, self-harm, hate, sexual content, harassment)\n- Computer vision annotation experience (detection, segmentation, OCR labeling)\n- Experience with disagreement analysis, rater calibration sessions, and quality sampling plans\n- Familiarity with experiment tracking and evaluation metrics tied to model performance improvement",{"h2":46,"desc":47},"Work Model, Location, and Schedule","This is a Remote, FULL_TIME role for candidates in the US, aligned to Boston talent and availability. You will work asynchronously with scheduled calibration and QA review sessions as needed to maintain consistent training data quality.",{"h2":49,"desc":50},"Compensation","Salary Range: 63360 to 126720 USD per YEAR. Compensation may vary based on evaluation scope, domain complexity (NLP, computer vision, content safety), and demonstrated quality leadership in LLM training pipelines.",{"h2":52,"desc":53},"How to Apply on Rex.zone","Apply through Rex.zone to be considered for senior data labeling jobs in Boston and similar remote roles across NLP, computer vision annotation, content safety labeling, and LLM evaluation programs. Your application should highlight examples of training data quality ownership, annotation guidelines compliance, and QA evaluation work that improved model performance.","AI Data Operations"]