Remote Data Labeling Jobs in Boston

Remote data labeling jobs in Boston at Rex.zone focus on creating and evaluating high-quality training data for AI/ML systems. You will label text, images, audio, and video; follow annotation guidelines compliance; and support LLM training pipelines through RLHF, prompt evaluation, content safety labeling, and QA evaluation. This role improves training data quality, model performance improvement, and large language model evaluation across NLP, computer vision annotation, and named entity recognition. Explore full-time remote opportunities supporting AI labs, tech startups, BPOs, and annotation vendors through Rex.zone.

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Job Heading: Remote Data Labeling Jobs in Boston

Title: Remote Data Labeling Jobs in Boston | 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, Prompt evaluation, QA evaluation, Annotation guidelines compliance, Training data quality, Large language model evaluation, Named entity recognition, Computer vision annotation, Content safety labeling, LLM training pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

You will deliver high-precision labeled datasets used to train and evaluate AI models. Projects may include LLM instruction-following tasks, RLHF preference ranking, prompt response grading, content safety labeling, and computer vision annotation. You will apply detailed annotation guidelines, resolve ambiguity through documented decision rules, and collaborate with QA to ensure consistent training data quality across batches.

What You Will Do

Core responsibilities include: annotating text/image/audio/video samples; performing QA evaluation and self-audits; executing RLHF tasks such as pairwise ranking and justification labeling; running prompt evaluation for helpfulness, harmlessness, and factuality; labeling named entities and relations for NLP; supporting computer vision annotation (bounding boxes, polygons, keypoints); applying content safety labeling policies; tracking annotation guidelines compliance; escalating edge cases with clear examples; and contributing to model performance improvement by improving label consistency and reducing noise in LLM training pipelines.

What We Are Looking For

You have experience delivering accurate annotations at production scale and can follow strict taxonomy and policy requirements. You are comfortable with ambiguity, can write concise rationales for labels, and can maintain consistency over long labeling sessions. You understand how labeling decisions affect large language model evaluation and downstream metrics, and you can work independently in a remote, full-time setting while meeting quality and throughput goals.

Preferred Domain Experience

Experience in one or more of the following is strongly preferred: NLP annotation (NER, sentiment, intent, semantic similarity); RLHF workflows (preference ranking, rubric-based grading); prompt evaluation and response quality scoring; content safety labeling for policy categories; computer vision annotation for detection/segmentation; speech/audio transcription or classification; and dataset QA processes such as gold sets, inter-annotator agreement, and error taxonomy reporting.

Tools and Workflows

You will work in web-based labeling platforms with task queues, embedded guidelines, and audit tooling. Typical workflows include calibration rounds, gold task validation, spot checks, disagreement resolution, and QA evaluation review. You will document edge cases, apply consistent decision rules, and support continuous improvement of annotation guidelines compliance to raise training data quality.

Remote Work and Location Notes

This is a Remote, FULL_TIME role aligned to US working norms. The job is listed for Boston search intent; work is performed remotely with reliable internet, secure workspace practices, and consistent availability for team sync and QA reviews.

Compensation

Salary range is USD 63360 to USD 126720 per YEAR, dependent on skills alignment, dataset domain complexity (NLP, computer vision annotation, content safety labeling), and demonstrated QA evaluation performance.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your resume and a brief summary of relevant data labeling, RLHF, and QA evaluation experience. Include examples of annotation guidelines compliance, training data quality improvements you contributed to, and any domain specialization in NLP, computer vision annotation, or content safety labeling.

Frequently Asked Questions

  • Q: Are these remote data labeling jobs in Boston fully remote?

    Yes. The Remote Type is Remote, and the work is performed remotely. The Boston reference supports local search intent while keeping the role explicitly remote.

  • Q: What kinds of tasks are included in data labeling for AI/ML?

    Tasks commonly include data labeling and data annotation for text, images, audio, and video; named entity recognition; computer vision annotation; content safety labeling; RLHF preference ranking; prompt evaluation; and QA evaluation to ensure training data quality for LLM training pipelines.

  • Q: What is RLHF and how does it relate to this role?

    RLHF (Reinforcement Learning from Human Feedback) uses human judgments—such as preference rankings and rubric-based scoring—to train reward models and improve model behavior. In this role, RLHF tasks help refine large language model evaluation and drive model performance improvement.

  • Q: Is this position contract, freelance, or full-time?

    This posting is for FULL_TIME employment. Rex.zone may also list remote, contract, or freelance roles separately depending on project needs.

  • Q: What experience level is required?

    The Experience Level is Mid-Senior. You should be able to follow complex annotation guidelines compliance requirements, deliver consistent labels, and contribute to QA evaluation processes.

  • Q: Which domains might I work on?

    Common domains include NLP (named entity recognition, classification, semantic similarity), computer vision annotation (detection/segmentation), content safety labeling, and large language model evaluation through prompt evaluation and RLHF workflows.

  • Q: How is quality measured in data labeling?

    Quality is measured through QA evaluation methods such as gold tasks, audits, inter-annotator agreement, rubric adherence, error taxonomy reporting, and consistency checks that improve training data quality for LLM training pipelines.

  • Q: Where do I apply?

    Apply through Rex.zone by submitting your resume and details about your data labeling, RLHF, prompt evaluation, and QA evaluation experience, including any specialization in NLP, computer vision annotation, or content safety labeling.

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