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# LLM Data Quality Analyst

- **Company:** US Tech Solutions, Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Large Language Models, Information Technology
- **Published:** June 5, 2026
- **Apply:** https://dejobs.org/x/x/1498D689BE23472796C22A7994803441/job/

## About the Role

* 3+ years of experience in Technical Writing, Quality Assurance or a related field.
 * Excellent writing and editing skills, with the ability to adapt to different styles and tones.
 * Strong analytical and problem-solving skills.
 * Ability to work independently and as part of a team.

Skills:

 * Data Quality
 * LLM processes like Pre-training, RLHF, SFT, Evals, * Bachelor's degree in English, Creative Writing, Linguistics, Science, Computer Science, or a related field.

## Description

* We are seeking a highly skilled data quality expert with a strong understanding of Large Language Models (LLMs) to join our team as a Quality Analyst for Human-created Data.
 * In this role, you will liaise closely with the highly technical modeling teams as well as vendor leads, understand the data needs of LLM modeling teams, and ensure that the data produced by expert content creators meets the highest quality standards.
 * You'll need a strong ability to understand LLM use cases, have a nuanced understanding of good versus great content, and be able to use quantitative and qualitative techniques to analyze LLM datasets.

Responsibilities:

 * Ensure high quality during task development:
 * Define data requirements based on a deep understanding of the modeling team goals and study of model loss patterns.
 * Design detailed instructions for content creator teams, illustrating nuanced differences with clear examples.
 * Rapidly iterate with content-production vendors on small batches of data to arrive at desired data quality.
 * Measure and demonstrate data quality during data collection:
 * Drive processes to deliver benchmark datasets for assessing data quality.
 * Benchmark vendor data quality against competitor models.
 * Use quantitative techniques to analyze vendor-produced data to assure high quality and drive rater pool optimization.
 * Audit datasets for quality issues and develop tools for accelerating qualitative analysis.
 * Use modeling and experimentation techniques to demonstrate data impact and identify blind spots.
 * Communicate on data quality:
 * Send out regular reports to project stakeholders on data quality.
 * Track key metrics such as iteration time and vendor quality audit results.
 * Author best practices and guideline documents (e.g. guidelines for teams wanting to work with vendors for human data, best practices for instruction design, etc).


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- [Give Your LLMs a Left Brain](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain)
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- [Lies, Damned Lies and Large Language Models](https://www.wearedevelopers.com/videos/1231-lies-damned-lies-and-large-language-models)
- [Data Privacy in LLMs: Challenges and Best Practices](https://www.wearedevelopers.com/videos/1218-data-privacy-in-llms-challenges-and-best-practices)
- [It's not easy being green](https://www.wearedevelopers.com/videos/558-it-s-not-easy-being-green)

## Related Articles

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- [Who Owns Your Content in the Age of LLMs?](https://www.wearedevelopers.com/magazine/610-who-owns-your-content-in-the-age-of-llms)
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- [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)