Senior Data Labeling Jobs in Minneapolis

Senior data labeling jobs in Minneapolis focus on building high-quality training datasets for AI/ML systems through data labeling, RLHF evaluation, and QA review. At Rex.zone, you will support large language model evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling workflows that improve model performance and training data quality. This remote, full-time role emphasizes annotation guidelines compliance, precise judgment calls, and measurable model performance improvement across NLP and multimodal pipelines.

Senior Data Labeling Jobs in Minneapolis

Title: Senior Data Labeling Specialist (Minneapolis) 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 evaluation, LLM evaluation, Prompt evaluation, Training data quality, Annotation guidelines compliance, QA evaluation, NLP annotation, Named entity recognition, Computer vision annotation, Content safety labeling, Taxonomy development Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will lead and execute senior data labeling work that directly supports LLM training pipelines and evaluation frameworks. Your day-to-day includes labeling and reviewing text and multimodal examples, running RLHF-style preference ranking, performing prompt evaluation for instruction-following quality, and conducting QA evaluation to ensure training data quality. You will apply annotation guidelines compliance, resolve edge cases, and help calibrate reviewer consistency so datasets are reliable for model performance improvement. Projects may span NLP, named entity recognition, computer vision annotation, and content safety labeling across multiple data sources and domains.

What You Will Do

You will deliver high-accuracy labels and reviews for complex tasks that require expert judgment and consistent rationale. You will perform RLHF evaluation (preference ranking and critique), prompt evaluation (helpfulness, truthfulness, verbosity control), and QA evaluation (spot checks, audits, disagreement analysis). You will annotate NLP datasets (classification, entity spans, relation hints) including named entity recognition, and support computer vision annotation (bounding boxes, polygons, attributes) when projects require. You will help maintain annotation guidelines compliance by documenting decisions, escalating ambiguous cases, and supporting taxonomy development for new labels. You will collaborate remotely with data operations, engineering, and QA to improve dataset usability for training and evaluation.

Required Qualifications

Mid-Senior experience in data labeling, data annotation, or AI evaluation workflows with a record of high-quality outputs. Demonstrated ability to follow and improve annotation guidelines compliance, including handling nuanced edge cases. Experience with at least one of: RLHF evaluation, LLM evaluation, or prompt evaluation. Strong writing and reasoning skills for providing consistent rationales, especially for preference judgments and safety decisions. Comfort working with QA evaluation methods such as audits, calibration sessions, and error categorization. Ability to work independently in a remote environment while meeting quality targets and delivery timelines.

Preferred Qualifications

Experience with named entity recognition and other NLP annotation tasks, including schema interpretation and span-level labeling. Exposure to computer vision annotation tools and formats (bounding boxes, polygons, keypoints) and quality checks. Familiarity with content safety labeling (policy interpretation, harm categories, sensitive content handling) and escalation practices. Understanding of how training data quality affects model performance improvement in LLM training pipelines. Experience supporting annotation vendors, BPO teams, or distributed reviewer cohorts through calibration and QA evaluation.

Work Model and Location

This is a Remote, FULL_TIME role listed under senior data labeling jobs in Minneapolis for candidates in the US. You will work with distributed teams supporting AI labs, tech startups, and data annotation vendors through Rex.zone workflows. Remote roles must remain explicitly marked Remote, and this position is Remote.

How Success Is Measured

High training data quality and consistent annotation guidelines compliance across tasks and edge cases. Strong inter-annotator agreement, reduced disagreement rates, and well-documented decision rationales. Reliable QA evaluation outcomes with clear error categorization and corrective actions. Effective RLHF evaluation and prompt evaluation signals that correlate with improved model behavior. On-time delivery with accurate, reproducible labels suitable for large language model evaluation and downstream training.

Why Rex.zone

Rex.zone connects skilled annotators and evaluators to remote AI/ML data operations work across NLP, computer vision, and content safety. You will contribute to real-world LLM training pipelines, evaluation programs, and data labeling projects that support production-grade AI systems.

Apply

Explore and apply to senior data labeling jobs in Minneapolis on Rex.zone. Prepare examples that demonstrate annotation guidelines compliance, QA evaluation experience, and your ability to perform RLHF evaluation or prompt evaluation consistently in high-stakes labeling workflows.

Frequently Asked Questions

Q: What are senior data labeling jobs in Minneapolis?
They are roles focused on producing and validating high-quality labeled datasets used in AI/ML training and evaluation. Work commonly includes data labeling, QA evaluation, RLHF evaluation, prompt evaluation, NLP annotation (including named entity recognition), computer vision annotation, and content safety labeling.
Q: Is this role remote or onsite in Minneapolis?
This role is Remote and based in the US. It is listed under senior data labeling jobs in Minneapolis while explicitly remaining marked Remote.
Q: What types of AI projects are supported?
Projects typically support large language model evaluation and LLM training pipelines, including RLHF evaluation, prompt evaluation, and training data quality improvements. Depending on need, work may span NLP, named entity recognition, computer vision annotation, and content safety labeling.
Q: What does RLHF evaluation mean in this job?
RLHF evaluation involves human preference judgments, ranking responses, and providing critiques that help optimize models toward helpfulness and safety. It is often paired with QA evaluation to ensure consistency and annotation guidelines compliance.
Q: What is prompt evaluation?
Prompt evaluation is the process of assessing model outputs for instruction-following, factuality, reasoning quality, style adherence, and safety. It provides structured feedback signals used in large language model evaluation and model performance improvement.
Q: What does QA evaluation include for data labeling?
QA evaluation includes audits, spot checks, calibration, disagreement analysis, and error categorization. The goal is to ensure training data quality and enforce annotation guidelines compliance before datasets are used in training or evaluation.
Q: Do I need computer vision annotation experience?
It is helpful but not required. Some projects may involve computer vision annotation (bounding boxes, polygons, attributes), while others focus on NLP annotation, named entity recognition, or content safety labeling.
Q: What employment type and experience level is this posting?
Employment Type is FULL_TIME and Experience Level is Mid-Senior.
Q: Which industries and employer types commonly use these skills?
Technology organizations including AI labs, tech startups, annotation vendors, and BPO teams commonly hire for senior data labeling. The work supports LLM training pipelines, evaluation programs, and content safety labeling operations.
Q: What skills should I highlight when applying via Rex.zone?
Highlight senior data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF evaluation, prompt evaluation, LLM evaluation, NLP annotation, named entity recognition, computer vision annotation, content safety labeling, and taxonomy development.

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