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# Research Engineer, Synthetic Data

- **Company:** CLERA, LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Automation of Tests, Python (Programming Language), Linux System Administration, Reinforcement Learning, Large Language Models, Data Pipelines, Docker
- **Published:** August 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4d8764cc542aa995

## About the Role

Required

 * 2-4 years of relevant engineering experience.
 * Proficiency in Python, Docker, and Linux environments.
 * Hands-on experience with synthetic data research methods.
 * Strong intuition for what makes synthetic data "good" - and an honest understanding of its limitations.
 * Demonstrated ability to build synthetic data pipelines end-to-end without a fully prescribed roadmap.
 * Experience working with environments, evaluations, and benchmarks.
 * Detail-oriented mindset for spotting subtle inconsistencies and edge cases in synthetic data.
 * Ability to reason from first principles about task design, scoring functions, and failure modes.
 * Comfort thriving in unstructured, early-stage environments where you define the path forward.
 * Strong written and verbal communication skills for async, cross-timezone collaboration.

Nice to Have

 * Background in reinforcement learning or post-training data for large language models.
 * Experience building reward signals, graders, or automated QA systems for agent tasks.
 * Prior work at an early-stage AI or ML startup.

## Description

* Build and maintain the synthetic data pipeline, turning domain-specific workflows into realistic, structured, and challenging training tasks for AI agents.
 * Collaborate with subject-matter experts across professional and technical domains to design high-quality synthetic tasks.
 * Design synthetic task generation methods that produce diverse, realistic, and learnable data at scale.
 * Build tooling to mutate, validate, and iteratively improve synthetic tasks.
 * Analyze model and agent performance on synthetic tasks to understand what they teach and where they break down.
 * Develop metrics to quantify synthetic task diversity, realism, learnability, and overall quality.


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