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# AI Training Data Annotation Consultant (Real...

- **Company:** US Tech Solutions, Inc.
- **Location:** Seattle, WA, United States
- **Salary:** $79,040.0 - $83,200.0
- **Contract:** Temporary contract
- **Skills:** Geographic Information Systems, Artificial Intelligence, Data Validation, Machine Learning, Data Classification, Large Language Models, GPT
- **Published:** May 30, 2026
- **Apply:** https://www.juju.com/job/00000000g3jxbp

## About the Role

+ **Annotation/QA discipline:** Prior experience with labeling, tagging, categorizing, or data classification work (professional or informal). Comfort with structured rules and consistency standards.

 + **AI tool literacy:** Hands-on experience with LLMs (ChatGPT, Claude, Gemini, etc.). Ability to identify when AI outputs are wrong, confusing, or incomplete.

 + **Attention to detail & rule-following:** Demonstrated ability to work within strict guidelines, apply rules consistently, and flag edge cases rather than guess.

 + **Feedback receptiveness:** Openness to critical feedback on your work; ability to iterate and improve based on coaching.

Skillsets:

 + Prior real estate industry experience (agent, appraiser, analyst, investor)

 + Data validation or quality assurance background

 + Experience evaluating AI/ML model outputs

 + Familiarity with structured research methodologies

 + Background in geographic information systems (GIS) or spatial analysis

Education:

 + Bachelor's degree in English, Linguistics or a related field.

## Description

+ Support annotation & QA for AI-driven home valuation. You'll establish data standards that train and evaluate the LLMs powering home value algorithm. We'll teach you real estate market knowledge; you bring strong annotation discipline and AI literacy., + Establish & enforce annotation standards for real estate datasets (neighborhoods, market conditions, property signals)

 + Review annotated work for accuracy & consistency; identify systemic errors and coach team on corrections

 + Evaluate how LLM outputs compared to human judgment; flag where models miss important nuances

 + Research unfamiliar neighborhoods using structured methodology; develop domain expertise over time

 + Spot & escalate data quality issues that would degrade valuation accuracy

 + Integrate LLMs into your workflows to accelerate research and improve annotation efficiency.


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