Remote Data Annotator Jobs in Phoenix

Remote data annotator jobs in Phoenix at Rex.zone focus on producing high-quality training data for AI systems across NLP, computer vision, and LLM training pipelines. You will perform data labeling, RLHF-based preference ranking, prompt evaluation, and QA evaluation to improve model performance and training data quality. This role follows annotation guidelines compliance, supports content safety labeling, and contributes to large language model evaluation used by AI labs, tech startups, and annotation vendors. Explore and apply through Rex.zone for full-time remote work aligned to real-world ML workflows and measurable quality standards.

Remote Data Annotator Jobs in Phoenix

Title: Remote Data Annotator Jobs in Phoenix

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About the Role

As a Remote Data Annotator supporting Phoenix-area talent, you will label and evaluate multimodal datasets used to train and validate AI/ML systems. Your work will include text classification, named entity recognition, prompt-response evaluation, RLHF preference ranking, and computer vision annotation (bounding boxes, polygons, and segmentation). You will apply detailed annotation guidelines compliance, document edge cases, and collaborate with QA to ensure training data quality and consistent decision-making across labelers.

What You Will Do

Core workflows include dataset review, task calibration, and ongoing quality checks. You will: (1) label data for NLP tasks such as intent classification, sentiment, toxicity, and named entity recognition; (2) perform large language model evaluation by scoring factuality, relevance, completeness, and safety; (3) execute RLHF-style ranking by comparing candidate model outputs; (4) annotate computer vision data using boxes, keypoints, and segmentation; (5) complete QA evaluation with inter-annotator agreement targets, audits, and rework when needed; (6) maintain clear notes on ambiguous cases to improve guideline quality.

Projects You May Support

Assignments may include content safety labeling for policy compliance, prompt evaluation for instruction-following, retrieval-augmented generation checks, conversation quality grading, and multilingual NLP labeling. Computer vision annotation projects may include retail shelf detection, document layout tagging, or autonomous perception datasets. Projects vary by client type, including AI labs, tech startups, BPOs, and annotation vendors, with consistent expectations around training data quality and model performance improvement.

Requirements

You should be comfortable working in structured queues with measurable accuracy targets. Requirements include strong written English, attention to detail, and the ability to follow complex annotation guidelines compliance. Experience with data labeling tools, QA evaluation practices, or prompt evaluation is preferred. Familiarity with NLP, computer vision annotation, RLHF workflows, and content safety labeling is a plus.

Quality and Performance Expectations

You will be evaluated on training data quality, consistency, and throughput. This includes meeting accuracy thresholds, reducing disagreement rates, maintaining clear rationale for decisions, and applying guideline updates quickly. You will participate in calibration sessions, respond to QA feedback, and help identify systematic errors that impact model performance improvement.

Work Model and Location

This is a full-time Remote role open to candidates in the US, aligned with Phoenix applicants seeking remote data annotator jobs. Work is performed online with secure access controls. Scheduling may align with project needs, including coordination with distributed teams across time zones.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile, availability, and relevant labeling experience. If selected, you may complete a short qualification covering annotation guidelines compliance, QA evaluation, and sample prompt evaluation or data labeling tasks. Qualified candidates may be matched to NLP, computer vision annotation, content safety labeling, or LLM training pipelines projects based on demonstrated accuracy.

Frequently Asked Questions

Q: What are remote data annotator jobs in Phoenix?
Remote data annotator jobs in Phoenix are online roles where you label and evaluate datasets used for AI/ML training. Work commonly includes data labeling for NLP and computer vision annotation, plus LLM tasks like prompt evaluation, RLHF preference ranking, and QA evaluation to improve training data quality.
Q: Is this role truly Remote?
Yes. Remote Type is Remote, and the role is performed online with project-specific tools and secure workflows while remaining based in the US.
Q: What types of annotation tasks will I do?
Typical tasks include named entity recognition, text classification, content safety labeling, computer vision annotation (boxes/segmentation), and large language model evaluation such as scoring responses for relevance, factuality, instruction-following, and safety.
Q: What is RLHF and how does it relate to this job?
RLHF (Reinforcement Learning from Human Feedback) uses human preference signals to improve model behavior. In this role, you may rank model outputs, compare responses, and provide structured judgments that feed RLHF training and evaluation.
Q: What tools or skills are most important?
Key skills include data annotation, data labeling, prompt evaluation, QA evaluation, annotation guidelines compliance, and attention to detail. Familiarity with NLP, computer vision annotation, named entity recognition, and content safety labeling is helpful for project matching.
Q: Is this full-time or contract work?
This posting is FULL_TIME. Some platforms and projects in the industry may also offer contract or freelance options, but this role is listed as full-time remote.
Q: What experience level is expected?
Experience Level is Mid-Senior. You should be able to follow complex guidelines, self-audit work, and contribute to calibration and QA processes that protect training data quality.
Q: How does Rex.zone fit into the hiring process?
Rex.zone is the platform where you explore the role, apply, and may be assessed for project fit. The workflow is designed to match annotators to AI labs, tech startups, BPOs, and annotation vendors running LLM training pipelines and evaluation programs.

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