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# Research Engineer, Synthetic Data

- **Company:** HUD
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Linux System Administration, Build Tools, Data Pipelines, Docker
- **Published:** July 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=cb521e4f325d4e6a

## About the Role

You may be a good fit if you have:

 * Proficiency in Python, Docker, and Linux environments
 * Have experience with synthetic data research methods - please elaborate in your application
 * Strong understanding of what "good synthetic data" means and its limitations
 * Built synthetic data pipelines end-to-end without a fully prescribed roadmap
 * Experience working on environments, evals, and benchmarks

Strong candidates may also:

 * Be detail-oriented and able to spot subtle inconsistencies or edge cases in synthetic data
 * Be able to reason from first principles about task design, scoring, and failure modes
 * Thrive in unstructured problem spaces
 * Early-stage startup experience with ability to work independently in fast-paced environments
 * Strong communication skills for remote collaboration across time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.

## Description

We're looking for Research Engineers to build our synthetic data pipeline. You'll turn domain-specific workflows into synthetic training tasks that are realistic enough to enough to teach useful behavior, structured enough to generate at scale, and difficult enough to expand model capabilities., * Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains

 * Design synthetic task generation methods that produce diverse, realistic, and learnable tasks
 * Build systems and tooling to mutate, validate, and improve synthetic tasks
 * Analyze model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail
 * Develop metrics to quantify and understand synthetic task diversity, realism, learnability, etc.


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