Lead Research Engineer, Data Quality

About the Role

This is a senior, hands-on technical leadership role owning the strategy and systems that measure, improve, and scale training data for frontier AI agents. You will sit at the intersection of research and engineering, leading a team that defines what high-quality agent training data looks like and building the infrastructure to enforce that bar at scale. The work directly shapes the post-training data that aligns AI models to real-world tasks.

What You'll Do

• Lead the data quality team in building evaluation systems across RL environments, synthetic data, benchmarks, and domain-specific workflows.

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

• Develop 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: validation pipelines, dashboards, internal tools, and feedback loops.

• Help build internal research taste around what