[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-portland":3},{"Ques":4,"Slug":31,"Header":32,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"A senior data annotator produces high-quality labeled data and evaluation signals used to train and assess models. Typical work includes data labeling, RLHF preference judgments, prompt evaluation, QA evaluation audits, and documenting edge cases to improve training data quality and model performance improvement.","What does a senior data annotator do in AI\u002FML training?",{"A":11,"Q":12},"Yes. The role is explicitly marked Remote and is performed remotely, with Portland used as a location modifier for search intent and candidate targeting.","Are these senior data annotator jobs in Portland remote?",{"A":14,"Q":15},"Projects commonly span NLP tasks like named entity recognition and text classification, large language model evaluation and RLHF, computer vision annotation, and content safety labeling for policy compliance and risk reduction in production AI systems.","What kinds of projects are included (NLP, computer vision, content safety, LLMs)?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) uses human preference data to guide model behavior. Senior annotators help create consistent preference judgments, rationales, and rubrics that improve instruction following, helpfulness, and safety in LLM training pipelines.","What is RLHF and why does it matter for this role?",{"A":20,"Q":21},"QA evaluation is the process of checking annotation correctness and consistency using audits, sampling, disagreement review, adjudication, and guideline compliance checks. It reduces label noise and improves dataset reliability.","What is QA evaluation in data annotation workflows?",{"A":23,"Q":24},"Common employer types include AI labs, tech startups, annotation vendors, and BPO organizations supporting enterprise AI programs. Roles may involve production labeling, evaluation operations, and content safety labeling at scale.","Which employers typically hire senior data annotators?",{"A":26,"Q":27},"Yes, it is FULL_TIME. The salary range is 63360 to 126720 USD per YEAR, as listed in the job metadata.","Is this a full-time role and what is the salary range?",{"A":29,"Q":30},"Use Rex.zone to explore and apply to senior data annotator jobs, including remote, full-time, contract, freelance, entry-level, and senior roles across NLP, computer vision, content safety, and LLM evaluation tracks.","How do I apply or find similar roles on Rex.zone?","senior-data-annotator-jobs-portland",{"desc":33,"title":34,"content":35},"Senior data annotator jobs in Portland focus on creating and evaluating training data that powers AI\u002FML systems on Rex.zone. In this full-time remote role, you will label and verify text, image, audio, and conversation data for large language model evaluation, RLHF, prompt evaluation, and content safety labeling. You will apply annotation guidelines compliance, run QA evaluation checks, and document edge cases that affect model performance improvement. This work supports LLM training pipelines, NLP tasks like named entity recognition, and computer vision annotation, helping AI labs, tech startups, annotation vendors, and BPO teams improve training data quality while scaling reliable human feedback.","Senior Data Annotator Jobs in Portland (Remote)",[36,39,42,45,48,51,54],{"h2":37,"desc":38},"Job Opening: Senior Data Annotator (Portland, Remote)","Title: Senior Data Annotator (Portland, Remote) | 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: Data annotation, Data labeling, RLHF, LLM evaluation, Prompt evaluation, QA evaluation, Annotation guidelines, Training data quality, Named entity recognition, Computer vision annotation, Content safety labeling, Taxonomy and ontology | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will deliver high-quality labeled datasets and human feedback signals for AI\u002FML training workflows. Your annotations will support supervised fine-tuning, RLHF preference data, evaluation benchmarks, and content policy enforcement. You will work from Portland in a remote capacity, collaborating asynchronously with project leads to calibrate decisions, reduce ambiguity, and improve dataset consistency across batches.",{"h2":43,"desc":44},"What You Will Do","Perform expert-level data labeling across modalities: NLP text classification, named entity recognition, summarization quality checks, and dialogue evaluation for LLMs\nCreate RLHF preference judgments and rationales for pairwise ranking, instruction following, and helpfulness\u002Fharmlessness criteria\nRun QA evaluation workflows: spot checks, inter-annotator agreement reviews, adjudication support, and error taxonomies\nAnnotate computer vision data when needed (bounding boxes, polygons, keypoints) and verify label accuracy against guidelines\nConduct prompt evaluation and model output grading for factuality, safety, tone, policy compliance, and refusal quality\nDocument edge cases, propose guideline updates, and produce clear feedback that improves training data quality and model performance improvement",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience performing data annotation, data labeling, or QA evaluation in production environments\nDemonstrated ability to interpret and apply complex annotation guidelines with high consistency\nHands-on experience with LLM evaluation, prompt evaluation, RLHF, or human feedback workflows\nStrong written reasoning skills for explaining labels, preferences, and safety decisions\nComfort working remotely with versioned instructions, batch tracking, and deadline-driven throughput expectations",{"h2":49,"desc":50},"Preferred Qualifications","Experience with named entity recognition, taxonomy\u002Fontology design, and annotation schema refinement\nExposure to content safety labeling, trust & safety policy enforcement, or red-teaming style evaluation\nExperience with computer vision annotation tools and visual QA processes\nFamiliarity with benchmark creation, evaluation rubrics, and calibration sessions to improve inter-annotator agreement",{"h2":52,"desc":53},"Tools and Workflow","Annotation platforms for task queues, labeling interfaces, and audit logs\nQA evaluation methods including sampling plans, disagreement analysis, and adjudication notes\nGuideline versioning and change management to keep annotation guidelines compliance consistent\nSecure handling of sensitive datasets and content safety labeling procedures where applicable",{"h2":55,"desc":56},"Employment Details","Remote Type: Remote (Portland-based candidates welcome; work is performed remotely)\nEmployment Type: FULL_TIME\nSalary Range: 63360 to 126720 USD per year (Pay Period: YEAR)\nIndustry: Technology | Job Function: Engineering\nApply\u002FExplore: Find and apply to relevant senior data annotator jobs on Rex.zone","AI Data Operations"]