Remote Data Labeling Jobs in Portland

Remote data labeling jobs in Portland focus on training-data creation and evaluation for modern AI systems on Rex.zone. As a Data Labeling Specialist, you will label and review text, images, and multimodal content used in large language model evaluation, RLHF pipelines, and computer vision annotation. Your work improves training data quality, annotation guidelines compliance, and model performance improvement through QA evaluation, prompt evaluation, and content safety labeling. You will collaborate asynchronously with QA leads and ML teams to produce reliable ground truth datasets used by AI labs, tech startups, and annotation vendors—full-time, remote, and impact-driven.

Remote Data Labeling Specialist (Portland)

Title: Remote Data Labeling Specialist (Portland) 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 labeling, data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines, training data quality, named entity recognition, computer vision annotation, content safety labeling Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

What You Will Do

You will execute data labeling and data annotation tasks across NLP and computer vision workflows, including named entity recognition, classification, ranking, and structured extraction. You will support RLHF by performing preference labeling, prompt evaluation, and response quality judgments aligned to rubric-driven evaluation. You will conduct QA evaluation by auditing labeled datasets, resolving edge cases, and enforcing annotation guidelines compliance to improve training data quality. You will contribute to content safety labeling, including policy-based tagging for sensitive content, toxicity, self-harm, and regulated topics. You will document decisions, escalate ambiguous examples, and help refine instructions so downstream LLM training pipelines and model evaluation remain consistent over time.

Project Types You May Support

LLM training pipelines: prompt/response grading, factuality checks, instruction-following evaluation, and rubric-based scoring. RLHF: pairwise preference labeling, reward model data generation, and disagreement resolution. NLP: named entity recognition, intent classification, sentiment and stance labeling, and taxonomy mapping. Computer vision annotation: bounding boxes, polygons, keypoints, and attribute tagging for images and video frames. Content safety labeling: policy interpretation, severity rating, and risk categorization for safety fine-tuning and trust & safety evaluation. QA evaluation: sampling plans, inter-annotator agreement checks, and targeted rework to reduce noise in ground truth datasets.

Requirements

Mid-Senior experience in data labeling, data annotation, or QA evaluation for ML datasets. Demonstrated ability to follow complex annotation guidelines, handle edge cases, and maintain high precision and recall in labeling outputs. Familiarity with NLP concepts (e.g., named entity recognition) and/or computer vision annotation fundamentals. Experience with LLM evaluation and prompt evaluation is strongly preferred; exposure to RLHF workflows is a plus. Strong written communication for documenting decisions, clarifying ambiguity, and collaborating asynchronously in a remote environment. Ability to manage throughput targets while maintaining consistent training data quality.

Quality Standards and How Success Is Measured

Success is measured through training data quality, annotation guidelines compliance, and QA evaluation outcomes. You will be evaluated on accuracy against gold sets, consistency across batches, and your ability to reduce rework by proactively resolving ambiguity. Additional signals include inter-annotator agreement, audit pass rates, and documentation quality for edge-case decisions. Strong performance leads to expanded scope across RLHF, content safety labeling, and large language model evaluation programs.

Work Model and Location Note

This is a Remote, FULL_TIME role. The job is aligned with remote data labeling jobs in Portland for candidates located in Portland, Oregon or able to work with US-based schedules and compliance requirements where applicable. Work is performed online using secure annotation tools and task platforms; you will collaborate with distributed teams supporting AI labs, tech startups, BPOs, and annotation vendors.

How to Apply on Rex.zone

Apply through Rex.zone to be considered for remote data labeling jobs in Portland. Prepare examples of annotation work (if available), details on NLP or computer vision experience, and any exposure to RLHF, LLM evaluation, prompt evaluation, or content safety labeling. Applications are reviewed for guideline adherence, attention to detail, and ability to produce high-quality labeled data under QA processes.

Frequently Asked Questions

Q: Are these truly remote data labeling jobs in Portland?
Yes. The role is Remote and designed for candidates in Portland or those seeking Portland-aligned remote opportunities, while performing work online through Rex.zone.
Q: What types of data will I label?
You may label text for NLP tasks (including named entity recognition), evaluate prompts and responses for LLM evaluation, and annotate images for computer vision annotation. Some projects also include content safety labeling.
Q: Does this role involve RLHF?
Many projects include RLHF-style workflows such as preference labeling, rubric scoring, and disagreement resolution to generate reward model training data.
Q: How is quality ensured?
Quality is ensured via annotation guidelines compliance, gold-standard checks, QA evaluation audits, sampling plans, and inter-annotator agreement reviews to maintain high training data quality.
Q: Is this full-time only, or are there other options?
This posting is FULL_TIME. On Rex.zone, you may also find contract or freelance data labeling work depending on project availability, but this role remains full-time and remote.
Q: What experience level is required?
This role is Mid-Senior and expects prior experience in data labeling, data annotation, or QA evaluation, ideally with exposure to LLM training pipelines, prompt evaluation, or content safety labeling.
Q: What employer types use this kind of work?
Projects commonly support AI labs, technology startups, annotation vendors, and BPOs that build and evaluate datasets for LLMs, NLP systems, and computer vision models.
Q: What skills should I emphasize in my application?
Emphasize data labeling, annotation guidelines, training data quality, QA evaluation, prompt evaluation, LLM evaluation, RLHF familiarity, named entity recognition, computer vision annotation, and content safety labeling.

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