[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-ai-data-annotation-jobs-silicon-valley":3},{"Ques":4,"Slug":25,"Header":26,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"They are mid-senior roles focused on creating and evaluating training data for AI\u002FML systems, often for LLM training pipelines. Work typically includes data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, and sometimes computer vision annotation and content safety labeling.","What are senior AI data annotation jobs in Silicon Valley?",{"A":11,"Q":12},"Yes. This posting is explicitly Remote and FULL_TIME, aligned to teams that operate in Silicon Valley programs but hire distributed talent.","Is this role remote and full-time?",{"A":14,"Q":15},"RLHF relies on human feedback signals such as preference ranking, rubric scoring, and rationale writing. These are specialized forms of annotation used to train and evaluate large language models and to drive model performance improvement.","How does RLHF relate to data annotation?",{"A":17,"Q":18},"Training data quality mindset, annotation guidelines compliance, RLHF and large language model evaluation experience, prompt evaluation competency, QA evaluation rigor, and strong written judgment. Domain skills like named entity recognition, computer vision annotation, and content safety labeling are also valuable.","What skills matter most for this job?",{"A":20,"Q":21},"AI labs, tech startups, enterprise AI teams, BPOs, and annotation vendors commonly hire for senior AI data annotation and evaluation work depending on scale and domain requirements.","What types of employers hire for these roles?",{"A":23,"Q":24},"Yes. While this specific posting is full-time, Rex.zone also features remote contract and freelance roles, as well as entry-level and senior openings across NLP, computer vision, and content safety programs.","Are there contract or freelance options on Rex.zone?","senior-ai-data-annotation-jobs-silicon-valley",{"desc":27,"title":28,"content":29},"Senior AI data annotation jobs in Silicon Valley focus on building high-quality training datasets for AI\u002FML systems—especially LLMs—through data labeling, RLHF, prompt evaluation, and QA evaluation. On Rex.zone, you will support real-world LLM training pipelines by applying annotation guidelines compliance, improving training data quality, and driving model performance improvement across NLP, computer vision annotation, and content safety labeling. This remote, full-time role emphasizes precise labeling decisions, rubric-based evaluation, and audit-ready documentation so AI labs, tech startups, and annotation vendors can ship safer, more reliable models. Explore and apply on Rex.zone to join mid-senior teams delivering measurable quality outcomes.","Senior AI Data Annotation Jobs in Silicon Valley (Remote, Full-Time)",[30,33,36,39,42,45,48,51,54],{"h2":31,"desc":32},"Job Heading: Senior AI Data Annotation Jobs in Silicon Valley","Title: Senior AI Data Annotation Specialist\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, training data quality, annotation guidelines compliance, large language model 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 the Role","You will lead mid-senior annotation workstreams that improve training data quality for production AI\u002FML models. Responsibilities span data labeling for NLP and CV, RLHF preference ranking, prompt evaluation, rubric-based QA evaluation, and content safety labeling. You will translate ambiguous policy into clear annotation guidelines, calibrate annotators with gold sets, and partner with engineering to connect labeling outcomes to model performance improvement.",{"h2":37,"desc":38},"Core Responsibilities","You will execute and\u002For oversee tasks such as: (1) designing and maintaining annotation guidelines compliance criteria and edge-case handling, (2) performing large language model evaluation via pairwise ranking, grading, and error taxonomy, (3) running prompt evaluation for instruction-following, helpfulness, harmlessness, and factuality, (4) completing named entity recognition and other structured NLP labeling, (5) delivering computer vision annotation including bounding boxes, polygons, and keypoints when required, (6) conducting content safety labeling across policy categories, (7) auditing batches for QA evaluation, inter-annotator agreement, and leakage risks, (8) creating gold data, adjudicating disagreements, and documenting decisions for traceability.",{"h2":40,"desc":41},"Workflows and Tooling","You will work in modern labeling and evaluation pipelines using task queues, versioned rubrics, and review layers. Common workflows include: sampling plans, calibration rounds, blind rechecks, and disagreement resolution. You should be comfortable with structured feedback loops where annotation outputs are measured against model metrics, and with secure handling of sensitive content common in content safety labeling.",{"h2":43,"desc":44},"Required Qualifications","Mid-senior experience delivering production-grade AI data annotation or evaluation. Demonstrated ability to follow and refine annotation guidelines compliance. Familiarity with RLHF and large language model evaluation (preference ranking, rubric scoring, rationale writing). Strong written judgment, consistency under ambiguity, and a quality-first mindset for training data quality. Comfort collaborating with engineering and research stakeholders in fast-moving AI\u002FML programs.",{"h2":46,"desc":47},"Preferred Qualifications","Experience with named entity recognition, taxonomy design, and error analysis. Exposure to computer vision annotation workflows and QA evaluation frameworks. Background in content safety labeling, policy interpretation, or trust-and-safety style reviews. Experience supporting AI labs, tech startups, BPOs, or annotation vendors delivering at scale.",{"h2":49,"desc":50},"What Success Looks Like","Clear, consistent labels and evaluations that reduce variance, increase inter-annotator agreement, and improve training data quality. Documented edge-case decisions that accelerate onboarding and reduce rework. QA evaluation results that reliably predict model performance improvement. Deliverables that are audit-ready and aligned to project rubrics, safety policies, and LLM training pipelines.",{"h2":52,"desc":53},"Why Rex.zone","Rex.zone connects remote professionals with AI\u002FML data operations work across NLP, computer vision annotation, RLHF, and content safety labeling. You will find full-time roles and also see related modifiers like contract, freelance, entry-level, and senior opportunities depending on the project needs and employer type.",{"h2":55,"desc":56},"How to Apply","Apply through Rex.zone with a resume highlighting RLHF, prompt evaluation, QA evaluation, and any domain depth in NLP, named entity recognition, computer vision annotation, or content safety labeling. Include examples of guideline writing, adjudication decisions, quality audits, and how your work contributed to model performance improvement.","AI Data Operations"]