Senior Data Labeling Jobs in Detroit

Senior data labeling jobs in Detroit at Rex.zone focus on building and evaluating training data for modern AI systems. You will lead annotation workflows that support LLM training pipelines, RLHF evaluation, prompt evaluation, and training data quality initiatives across NLP, computer vision annotation, named entity recognition, and content safety labeling. This remote, full-time role emphasizes annotation guidelines compliance, QA evaluation, and measurable model performance improvement through high-quality labeled datasets and robust review operations.

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Job Overview

Title: Senior Data Labeling Specialist (Detroit) 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, data labeling QA, annotation guidelines, RLHF, prompt evaluation, LLM evaluation, NLP labeling, named entity recognition, computer vision annotation, content safety labeling, training data quality, dataset auditing Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will operate as a senior contributor in data labeling jobs supporting Detroit-based and nationwide AI programs while working remotely through Rex.zone. Your focus is end-to-end annotation execution and review: translating taxonomy and policy into clear labeling instructions, enforcing annotation guidelines compliance, running QA evaluation loops, and driving training data quality improvements for large language model evaluation and model performance improvement. Projects may span NLP labeling (intent, sentiment, NER), computer vision annotation (bbox, polygon, keypoints), content safety labeling, and RLHF-style preference and ranking tasks.

Key Responsibilities

You will: (1) Lead complex data labeling workflows for LLM training pipelines, including prompt evaluation and RLHF evaluation tasks, (2) Create and iterate annotation guidelines, edge-case handling rules, and labeling taxonomies, (3) Perform QA evaluation using sampling plans, inter-annotator agreement checks, and targeted audits, (4) Triage ambiguous items and coordinate adjudication decisions to maintain consistent ground truth, (5) Monitor training data quality signals and provide feedback that supports model performance improvement, (6) Support dataset curation, deduplication guidance, and coverage analysis for Detroit-focused and general-domain datasets, (7) Document decisions for reproducibility and compliance in content safety labeling programs.

Required Qualifications

You have: (1) Mid-senior experience in data labeling or data annotation operations, including review and adjudication, (2) Hands-on familiarity with QA evaluation methods for labeled datasets (sampling, error taxonomies, agreement metrics), (3) Proven ability to write and maintain annotation guidelines with strong edge-case reasoning, (4) Experience with at least one domain: NLP labeling, named entity recognition, computer vision annotation, or content safety labeling, (5) Comfort working with large task queues, rubric-driven evaluation, and quality targets in production annotation workflows, (6) Strong written communication for guideline clarity and audit trails.

Preferred Qualifications

Preferred: (1) Prior exposure to RLHF, preference ranking, or prompt evaluation for large language model evaluation, (2) Experience with dataset auditing for training data quality and bias/error analysis, (3) Familiarity with annotation vendor operations, BPO workflows, or scaling review processes, (4) Comfort partnering with engineering or research stakeholders to connect labeling decisions to model performance improvement.

Tools and Workflows You May Use

Depending on project needs, you may use labeling platforms and review consoles, versioned guideline documents, QA dashboards, sampling tools, and evaluation rubrics. Work typically includes batch annotation, golden set calibration, disagreement resolution, and structured feedback loops tied to LLM training pipelines and production data labeling QA.

What Success Looks Like

Success means: (1) Consistently high training data quality with clear, enforceable annotation guidelines compliance, (2) Reduced rework through strong QA evaluation and adjudication rigor, (3) Faster iteration cycles for prompt evaluation and RLHF evaluation programs, (4) Traceable documentation that helps teams connect labeled data changes to model performance improvement.

Work Arrangement

This is a Remote, FULL_TIME position in the US, aligned to the Detroit job search intent while supporting distributed project teams. The role may collaborate with AI labs, tech startups, annotation vendors, and BPO-style delivery organizations through Rex.zone depending on program requirements.

How to Apply

Apply through Rex.zone and complete the role-relevant screening steps (labeling scenario questions, guideline comprehension, and QA evaluation exercises where applicable). Ensure your experience highlights senior data labeling ownership, training data quality work, and any RLHF, prompt evaluation, NLP labeling, computer vision annotation, or content safety labeling experience.

Frequently Asked Questions

  • Q: Are these senior data labeling jobs in Detroit remote?

    Yes. This posting is explicitly marked Remote and is open to qualified candidates in the US while aligning to Detroit-focused search intent.

  • Q: Is this role full-time or contract/freelance?

    This role is FULL_TIME. Rex.zone may also list contract and freelance roles separately, but this posting is full-time.

  • Q: What types of labeling tasks are included?

    Work can include RLHF evaluation, prompt evaluation, QA evaluation, named entity recognition, NLP labeling, computer vision annotation, and content safety labeling, depending on the project.

  • Q: What does training data quality mean in this job?

    Training data quality refers to correctness, consistency, coverage, and guideline adherence of labeled data, measured through audits, sampling, inter-annotator agreement, and targeted error analysis that supports model performance improvement.

  • Q: What experience level is expected?

    The experience level is Mid-Senior, with expectations of independently leading guidelines, QA evaluation processes, and adjudication for complex datasets.

  • Q: How does this role connect to LLM training pipelines?

    You will help create and validate labeled datasets and evaluation sets used for large language model evaluation, RLHF-style preference ranking, and prompt evaluation, which directly influence model behavior and performance.

  • Q: What skills should I highlight to match this posting?

    Highlight senior data labeling, data labeling QA, annotation guidelines, RLHF, prompt evaluation, LLM evaluation, NLP labeling, named entity recognition, computer vision annotation, content safety labeling, training data quality, and dataset auditing.

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