Senior Data Annotation Jobs in Chicago

Senior data annotation jobs in Chicago focus on building and evaluating training datasets for modern AI systems, including LLM training pipelines, RLHF, and multimodal annotation. On Rex.zone, you will lead data labeling workflows that improve training data quality, annotation guidelines compliance, and model performance improvement across NLP, computer vision, and content safety labeling. This remote, full-time role partners with engineers and QA teams to deliver consistent ground truth, conduct prompt evaluation and LLM response rating, and run rigorous QA evaluation so models learn reliably at scale.

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LinkedIn Job Metadata

Title: Senior Data Annotation 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 annotation, Data labeling, RLHF, QA evaluation, Prompt 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 own end-to-end senior data annotation workflows for AI/ML model development, with a strong focus on training data quality and repeatable labeling operations. The work includes NLP labeling (classification, summarization, named entity recognition), RLHF-style preference ranking, prompt evaluation, and LLM output evaluation, along with computer vision annotation such as bounding boxes, polygons, segmentation, and keypoints. You will help design and enforce annotation guidelines, calibrate raters, run sampling plans, analyze disagreement, and partner with engineering to ship datasets that measurably improve model performance.

What You Will Do

Plan and lead data labeling projects across NLP, CV, and content safety labeling; execute RLHF and LLM evaluation tasks including pairwise ranking and rubric-based grading; create and iterate annotation guidelines with clear edge cases and decision rules; run QA evaluation programs (gold sets, audits, inter-annotator agreement) to ensure annotation guidelines compliance; investigate label noise and perform error analysis tied to model performance improvement; collaborate with engineering on tooling, schema design, and dataset versioning for LLM training pipelines; mentor annotators, conduct calibration sessions, and drive consistent labeling outcomes across distributed teams; document dataset limitations, bias risks, and safety considerations to support responsible AI development.

Required Qualifications

3+ years in data annotation, data labeling, or AI evaluation with measurable QA ownership; experience with RLHF, prompt evaluation, or LLM response rating; strong knowledge of annotation guidelines design and annotation guidelines compliance; hands-on understanding of training data quality metrics (agreement, precision/recall sampling, error taxonomies); familiarity with NLP tasks (NER, intent, sentiment, classification) and/or computer vision annotation (boxes, polygons, segmentation); comfort working in remote workflows with ticketing, documentation, and dataset version control practices; ability to communicate clearly with engineering and stakeholders and translate model errors into labeling improvements.

Preferred Qualifications

Experience with content safety labeling (policy-based decisions, harm categories, sensitive content triage); background in evaluation design for LLMs (rubrics, test sets, adversarial prompts); exposure to annotation platforms and QA tooling (review queues, audit trails, consensus workflows); ability to write basic scripts or queries for analysis (e.g., Python or SQL) to support QA evaluation; experience supporting AI labs, tech startups, annotation vendors, or BPO delivery models while maintaining consistent quality.

Work Location and Remote Setup

This is a Remote, FULL_TIME position aligned to Chicago and US hiring requirements. You will collaborate with cross-functional teams across time zones using standardized labeling operations, documentation, and QA evaluation processes. Remote roles must remain Remote, and this role is explicitly Remote.

Compensation

Salary range is USD 63360 to 126720 per YEAR, depending on scope, domain complexity (NLP, computer vision, content safety), and demonstrated leadership in training data quality and QA evaluation.

How to Apply on Rex.zone

Apply through Rex.zone with a resume highlighting senior data annotation leadership, RLHF or prompt evaluation experience, and examples of training data quality improvements. Include notes on annotation guidelines you authored, QA evaluation methods you ran, and the types of LLM training pipelines or computer vision annotation projects you supported.

Frequently Asked Questions

  • Q: Are these senior data annotation jobs in Chicago remote or onsite?

    This Rex.zone role is Remote and aligned to Chicago/US hiring requirements. The day-to-day work is performed remotely using annotation tools, QA workflows, and documentation standards.

  • Q: What does senior data annotation mean in an AI/ML context?

    Senior data annotation typically includes leading labeling operations, writing and enforcing annotation guidelines, running QA evaluation (audits, gold sets, agreement analysis), and improving training data quality for NLP, computer vision, and LLM training pipelines.

  • Q: Does the role include RLHF and LLM evaluation tasks?

    Yes. The role covers RLHF-style preference ranking, rubric-based grading, prompt evaluation, and other LLM evaluation tasks that support model performance improvement.

  • Q: What domains are commonly covered in this job?

    Common domains include NLP (named entity recognition, classification), computer vision annotation (bounding boxes, segmentation), and content safety labeling, plus dataset QA and evaluation design.

  • Q: Is this position full-time and what is the experience level?

    Yes. Employment Type is FULL_TIME and Experience Level is Mid-Senior, matching the senior data annotation job intent while keeping the provided defaults unchanged.

  • Q: What skills should I highlight to be competitive?

    Highlight senior data annotation, data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines.

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50+Countries Represented

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