Lead Research Engineer, Data Quality

About the Role

This is a senior technical leadership role on the data quality team at an early-stage AI infrastructure company focused on building and scaling reinforcement learning environments for frontier model training. You will own the strategy and systems that measure and improve training data quality, shaping research culture around what makes agent data genuinely useful rather than just superficially correct.

What You'll Do

• Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.

• Define the data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.

• Develop new 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.

• Turn qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.

• Mentor research engineers to maintain a high bar for technical