Senior AI Data Annotation Jobs in Minneapolis

Senior AI data annotation jobs in Minneapolis focus on training-data quality for modern AI/ML systems on Rex.zone. In this remote full-time role, you will label and evaluate data for large language model evaluation, RLHF, prompt evaluation, and QA review across NLP and computer vision workflows. Your work improves model performance through consistent annotation guidelines compliance, error analysis, and structured feedback loops for LLM training pipelines. You will collaborate with engineers and project leads to deliver high-precision datasets for named entity recognition, content safety labeling, and multimodal tasks while maintaining throughput, accuracy, and documentation standards.

Job Heading: Senior AI Data Annotation Specialist (Minneapolis, Remote)

Title: Senior AI Data Annotation Specialist (Minneapolis, Remote) 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: AI data annotation, data labeling, RLHF, LLM evaluation, prompt evaluation, QA evaluation, annotation guidelines, training data quality, named entity recognition, computer vision annotation, content safety labeling, dataset auditing, inter-annotator agreement Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

What You Will Do

You will execute senior-level AI data annotation and evaluation work across NLP, LLM, and computer vision projects. You will label text, image, and multimodal data; perform RLHF preference ranking; run prompt evaluation for instruction-following; and conduct QA evaluation to verify training data quality. You will write and refine annotation guidelines, resolve edge cases, and document decisions for consistent annotation guidelines compliance. You will perform dataset auditing, error analysis, and sampling plans to improve model performance improvement outcomes. You will support named entity recognition, intent classification, content safety labeling, and policy-driven moderation datasets. You will collaborate with engineering and operations to improve tooling, throughput, and inter-annotator agreement.

Core Workflows You Will Own

You will contribute to large language model evaluation through rubric-based scoring, pairwise comparisons, and RLHF feedback signals. You will complete data labeling tasks including taxonomy tagging, span labeling, and structured extraction for named entity recognition. You will handle computer vision annotation such as bounding boxes, polygons, segmentation masks, and keypoints when required by project scope. You will perform prompt evaluation and response grading for helpfulness, correctness, and safety, including adversarial and red-team style prompts where applicable. You will run QA evaluation using gold sets, blind reviews, and reconciliation, and you will track training data quality trends over time.

Requirements

You have experience in AI data annotation, data labeling, or model evaluation in production-like workflows. You can follow and improve annotation guidelines, handle ambiguous cases, and maintain high accuracy under tight iteration cycles. You understand concepts behind RLHF, LLM evaluation, and prompt evaluation, and you can apply rubrics consistently. You are comfortable with spreadsheet-style tooling and web labeling platforms, and you can produce clear documentation for QA evaluation and dataset auditing. You can communicate decisions and quality issues clearly to cross-functional stakeholders in a remote environment.

Preferred Qualifications

Experience with NLP tasks such as named entity recognition, relation extraction, and text classification. Exposure to computer vision annotation and quality control practices. Familiarity with content safety labeling, policy evaluation, and harm taxonomies. Understanding of inter-annotator agreement, confusion matrices, and basic sampling strategies for training data quality. Experience contributing feedback to improve labeling tools, reviewer workflows, or rubric design for large language model evaluation.

Quality Standards and Compliance

You will be expected to meet defined accuracy targets and throughput goals while maintaining annotation guidelines compliance. You will participate in calibration sessions, audits, and reconciliation to maintain consistent labeling. You will document edge-case decisions to reduce drift and improve training data quality. You will handle sensitive content when required by content safety labeling projects and follow access controls and confidentiality requirements.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile and relevant experience in AI data annotation, RLHF evaluation, prompt evaluation, and QA evaluation. Highlight any domain expertise in NLP, computer vision annotation, named entity recognition, or content safety labeling. Include examples of how your work improved training data quality or contributed to model performance improvement through careful dataset auditing and guideline refinement.

Frequently Asked Questions

Q: Is this a remote role for candidates in Minneapolis?
Yes. The role is marked Remote and is aligned to Minneapolis for search intent and regional relevance, while allowing remote work across the US per Rex.zone requirements.
Q: What makes this a senior AI data annotation job?
Senior scope includes owning training data quality outcomes, leading guideline refinement, handling complex edge cases, performing QA evaluation and dataset auditing, and contributing to RLHF and large language model evaluation workflows.
Q: Which domains are covered: NLP, computer vision, or content safety?
Projects may include NLP (named entity recognition and text classification), computer vision annotation (boxes, polygons, segmentation), and content safety labeling depending on client needs and LLM training pipelines.
Q: What is RLHF and how does it appear in daily work?
RLHF is Reinforcement Learning from Human Feedback. In practice, you will do preference ranking, rubric scoring, and pairwise comparisons that create feedback signals used to improve model behavior and instruction-following.
Q: What does QA evaluation mean for data labeling?
QA evaluation includes gold-set checks, blind reviews, reconciliation, calibration, and audit sampling to ensure annotation guidelines compliance and consistent training data quality.
Q: Is this full-time or contract/freelance?
This posting is FULL_TIME. Rex.zone may also list contract or freelance roles separately, but this job is explicitly full-time and remote.
Q: What tools will I use?
You will typically use web-based labeling platforms, rubric scoring interfaces for LLM evaluation, and spreadsheet-style trackers for audits and quality reporting; tool specifics vary by project.

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