[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-ai-data-annotation-jobs-philadelphia":3},{"Ques":4,"Slug":31,"Header":32,"job_category":63},{"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 supports Philadelphia-area recruiting needs while allowing you to work from anywhere in the US.","Is this role remote even though it targets Philadelphia hiring demand?",{"A":11,"Q":12},"Senior scope includes adjudication, QA evaluation, calibration, guideline improvements, and ownership of training data quality outcomes that impact large language model evaluation and model performance improvement.","What makes this a senior AI data annotation role?",{"A":14,"Q":15},"The work commonly spans NLP (named entity recognition, classification, prompt evaluation), LLM training pipelines (RLHF preference ranking and response grading), computer vision annotation, and content safety labeling.","Which AI domains are most relevant for this job?",{"A":17,"Q":18},"This posting is FULL_TIME. Rex.zone may also list contract or freelance roles, but this specific job is full-time.","Is this position full-time or contract\u002Ffreelance?",{"A":20,"Q":21},"You will use annotation and review workflows aligned to labeling guidelines, sampling-based QA evaluation, disagreement resolution, and structured feedback loops that produce reliable training signals for model training and evaluation.","What tools or workflows will I use?",{"A":23,"Q":24},"RLHF tasks often include preference ranking between model responses, grading outputs against rubrics, identifying failure modes, and providing consistent human feedback used to tune models for helpfulness, safety, and instruction-following.","What does RLHF work look like day to day?",{"A":26,"Q":27},"AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and understanding of LLM training pipelines.","What are the top skills needed to succeed?",{"A":29,"Q":30},"The listed salary range is 63360 to 126720 USD per year, depending on skills, scope, and project requirements.","What salary range is listed for this role?","senior-ai-data-annotation-jobs-philadelphia",{"desc":33,"title":34,"content":35},"Senior AI data annotation jobs in Philadelphia focused on training data quality for large language models and multimodal AI systems. On Rex.zone, you will support real-world AI\u002FML training workflows through data labeling, RLHF, prompt evaluation, QA evaluation, and annotation guidelines compliance to drive model performance improvement. This remote, full-time role partners with AI labs, tech startups, and annotation vendors to produce high-quality datasets for NLP and computer vision, including content safety labeling and named entity recognition. Explore and apply on Rex.zone to join production-grade LLM training pipelines.","Senior AI Data Annotation Jobs in Philadelphia",[36,39,42,45,48,51,54,57,60],{"h2":37,"desc":38},"Job Heading: Senior AI Data Annotation Jobs in Philadelphia","Title: Senior AI Data Annotation Specialist (Philadelphia)\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: AI data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines, 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":40,"desc":41},"About the Role","As a Senior AI Data Annotation Specialist supporting Philadelphia hiring demand (remote), you will create, review, and improve training datasets used to train and evaluate large language models and multimodal AI systems. You will apply detailed annotation guidelines, perform QA evaluation and audits, calibrate labeling consistency, and deliver actionable feedback that improves model performance and reduces edge-case failures across NLP, computer vision, and content safety workflows.",{"h2":43,"desc":44},"What You Will Work On","You will contribute to LLM training pipelines by producing and validating labeled data and evaluation signals. Typical work includes RLHF preference ranking, prompt evaluation, response grading, named entity recognition and entity linking, intent classification, document and conversation annotation, computer vision bounding boxes and segmentation, and content safety labeling for policy compliance. You will also help define edge cases, maintain annotation guidelines compliance, and support training data quality initiatives across projects.",{"h2":46,"desc":47},"Responsibilities","Own end-to-end data labeling tasks from onboarding to delivery; execute RLHF and QA evaluation to generate reliable human feedback signals; perform senior-level review, adjudication, and disagreement resolution; improve annotation guidelines and provide clarifications for ambiguous cases; run training data quality checks, sampling plans, and error taxonomies; partner with engineers and project leads to align labeling to model objectives; document decisions to ensure reproducibility, auditability, and consistent model evaluation.",{"h2":49,"desc":50},"Required Qualifications","Demonstrated experience in AI data annotation and data labeling workflows; strong judgment for prompt evaluation, response grading, and content safety labeling; experience applying annotation guidelines compliance with high accuracy; familiarity with NLP tasks such as named entity recognition and text classification; ability to perform QA evaluation, inter-annotator agreement calibration, and error analysis; strong written communication for documenting edge cases and guideline updates; comfortable working in remote, full-time production environments.",{"h2":52,"desc":53},"Preferred Qualifications","Hands-on exposure to RLHF, preference ranking, or LLM evaluation frameworks; experience with computer vision annotation (bounding boxes, polygons, segmentation, keypoints); background in content moderation, trust and safety, or policy labeling; understanding of dataset shift, bias, and data quality impacts on model performance improvement; experience collaborating with AI labs, tech startups, BPOs, or annotation vendors; familiarity with secure handling of sensitive data and confidentiality requirements.",{"h2":55,"desc":56},"Work Style and Collaboration","You will work asynchronously with clear targets and quality standards, while joining calibration sessions to align labeling decisions across the team. Success in this role depends on careful reading, consistent application of guidelines, willingness to flag ambiguity, and the ability to translate qualitative judgments into structured labels that help models learn.",{"h2":58,"desc":59},"Why Rex.zone","Rex.zone connects experienced annotators with remote, full-time opportunities supporting real AI\u002FML production systems. You will work on projects spanning NLP, computer vision, and content safety, with an emphasis on training data quality, reproducible evaluation, and measurable model performance improvement.",{"h2":61,"desc":62},"How to Apply","Apply via Rex.zone with a resume that highlights AI data annotation, data labeling, RLHF, QA evaluation, and domain experience (NLP, computer vision, or content safety). Include examples of guideline-driven work, calibration participation, and how you improved training data quality or reduced annotation errors in prior projects.","AI Data Operations"]