Research Engineer, QC Automation

The role

You will build automated quality control systems for training data used to train and evaluate AI agents. You will work on evaluation metrics, experiments, and standards for data quality, and work with data vendors to resolve quality problems.

What you will do

• Automate quality control for training data generated through the platform.
• Build quality systems grounded in human judgment, with limited reliance on large language models.
• Define and enforce standards for training data quality.
• Design experiments and metrics to evaluate agent outputs.
• Work with data vendors to investigate quality issues and diagnose agent failure modes.
• Improve data generation processes with vendors.
• Turn quality findings into sampling strategies and validation pipelines.
• Incorporate quality insights into infrastructure tools and vendor workflows.

Required qualifications

• Two to four years of experience in research engineering or a related technical role, especially in quality control automation.
• A technical degree.
• Proficiency with Python, Docker, and Linux.
• Experience independently building scalable data validation pipelines and end-to-end automated QA or QC systems.
• Experience with benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories.
• Ability to define and measure training data quality and design QC systems that use human judgment.
• Experience designing evaluation metrics and experiments and collaborating with data vendors to resolve quality problems.
• Knowledge of statistics.
• Strong communication skills.
• Curiosity and comfort working independently in an early-stage environment.

Before you apply

• Visa sponsorship is available.

About the company

clera is an AI infrastructure company focused on reinforcement learning environments and evaluation. The team builds systems to improve the reliability of data used to train and evaluate AI agents.