Member of Technical Staff - Data Quality Engineer (Post-training)

Reflection AI is dedicated to building open superintelligence accessible to everyone. As a Data Quality Engineer, you will ensure that the data used for training and evaluating AI models meets high standards of quality and reliability, while collaborating with researchers to develop measurable quality signals.

Responsibilities:

• Own upstream data quality for LLM post-training and evaluation by analyzing expert-developed datasets and operationalizing quality standards for reasoning, alignment, and agentic use cases
• Partner closely with research and post-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors
• Design, validate, and scale automated QA methods, including LLM-as-a-Judge frameworks, to reliably measure data quality across large campaigns
• Build reusable QA pipelines that reliably deliver high-quality data to post-training teams for model training and evaluation
• Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria