Lead Research Engineer, Data Quality

About the Role

This is a senior individual-contributor and team-lead role sitting at the intersection of AI evaluation, synthetic data, and reinforcement learning infrastructure. You will own the strategy and systems that measure and improve training data quality for frontier AI agents, shaping internal research culture around what makes agent data genuinely useful rather than superficially correct.

What You'll Do

• Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data pipelines, benchmarks, and domain-specific workflows.
• Define the data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.
• Develop and implement methods for validating synthetic data at scale, including failure-mode analysis, task mutation checks, and trajectory auditing.
• Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows.
• Translate qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.