Senior Data Labeling Jobs in Chicago

Senior data labeling professionals in Chicago help power Rex.zone AI training workflows by producing high-quality labeled datasets and evaluation signals for large language models and computer vision systems. This full-time remote role focuses on training data quality, annotation guidelines compliance, and model performance improvement through RLHF, prompt evaluation, QA evaluation, named entity recognition, content safety labeling, and end-to-end LLM training pipelines. You will lead complex labeling projects, improve label accuracy, and mentor annotators while partnering with engineering and product teams to ship reliable AI systems.

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Senior Data Labeling Jobs in Chicago

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: Senior data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines, training data quality, 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 lead senior-level data labeling and evaluation work for AI/ML systems used by AI labs, tech startups, and annotation vendors through Rex.zone. Your focus is to produce trustworthy ground truth and preference data, run QA evaluation, and continuously improve training data quality. You will work across NLP and computer vision annotation tasks, including named entity recognition, prompt evaluation for LLMs, RLHF preference ranking, and content safety labeling. The role is remote, full-time, and centered on measurable model performance improvement.

What You Will Do

Deliver senior data labeling for NLP, LLM evaluation, and computer vision annotation projects; apply and refine annotation guidelines compliance and edge-case handling; execute RLHF workflows (pairwise preference, ranking, justification) and prompt evaluation; run QA evaluation using sampling plans, adjudication, and error taxonomy; perform named entity recognition and structured extraction labeling with consistent span boundaries; label content safety policy categories (hate, harassment, self-harm, sexual content, violence) with calibrated severity; analyze disagreement patterns and drive label accuracy improvements with targeted retraining; coordinate with engineering on dataset specs, schema, and acceptance criteria; maintain documentation for labeling instructions, decision logs, and versioned guideline updates; mentor peers by reviewing work, providing feedback, and escalating ambiguous cases.

Required Qualifications

Mid-senior experience in data labeling, data annotation, or AI/ML evaluation; strong command of annotation guidelines and consistent decision-making under ambiguity; hands-on experience with QA evaluation practices such as gold sets, inter-annotator agreement, and adjudication; familiarity with RLHF concepts, preference labeling, and prompt evaluation for large language models; experience with named entity recognition or other NLP labeling schemas; ability to meet throughput targets without compromising training data quality; excellent written communication for clear rationales and decision logs.

Preferred Qualifications

Experience with computer vision annotation (bounding boxes, polygons, keypoints, segmentation) and quality checks; exposure to content safety labeling and policy-based decisioning; experience supporting LLM training pipelines, dataset curation, or model evaluation benchmarks; ability to create error taxonomies and run root-cause analysis for label quality; familiarity with data operations metrics (precision/recall proxies, rework rate, disagreement rate) and continuous improvement.

Workflows and Tools You Will Use

Annotation platforms and queue-based task routing; guideline versioning and change control; gold data creation, calibration tasks, and spot checks; adjudication workflows for complex edge cases; structured schemas for NLP and evaluation rubrics for LLM outputs; privacy-aware handling of sensitive content and content safety labeling protocols.

Why This Role on Rex.zone

Rex.zone connects senior data labeling professionals with real AI training needs across NLP, computer vision, and LLM evaluation. You will work on projects that require careful annotation guidelines compliance, reliable QA evaluation, and high-impact training data quality improvements. This is a remote, full-time opportunity aligned with Chicago-based candidates seeking senior data labeling jobs while working with distributed AI teams.

How to Apply

Apply through Rex.zone with a short summary of your senior data labeling experience, including RLHF or prompt evaluation work, QA evaluation practices you have used, and examples of annotation guidelines you have followed or improved. Highlight domains you have supported (NLP, computer vision, content safety) and the ways you contributed to model performance improvement.

Frequently Asked Questions

  • Q: Is this role remote or onsite in Chicago?

    This role is Remote. It is aligned with candidates searching for senior data labeling jobs in Chicago, but the work is performed remotely for Rex.zone via Rex.zone workflows.

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

    This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance roles, but this specific role is full-time.

  • Q: What kinds of tasks are included in senior data labeling?

    Typical tasks include training data quality improvement, annotation guidelines compliance, QA evaluation, adjudication, RLHF preference labeling, prompt evaluation for LLMs, named entity recognition, computer vision annotation, and content safety labeling.

  • Q: What is RLHF and how does it relate to data labeling?

    RLHF (Reinforcement Learning from Human Feedback) uses human preference signals to improve model behavior. In data labeling workflows, you may rank responses, choose preferred outputs, provide rationales, and follow evaluation rubrics to generate high-quality preference data for LLM training pipelines.

  • Q: What skills should I include for a senior data labeling application?

    Include skills like senior data labeling, training data quality, annotation guidelines, QA evaluation, prompt evaluation, RLHF, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines.

  • Q: How is quality measured in this role?

    Quality is typically measured via guideline adherence, gold set performance, spot-check results, disagreement rates, adjudication outcomes, and reductions in recurring error types that impact model performance improvement.

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