Lead Research Engineer, Data Quality

Lead the data quality team at an early-stage AI evaluation company, building the strategy and systems used to assess and improve training data for AI agents. You will define quality standards, develop scalable validation methods, and translate research insights into production tools. This role offers the opportunity to shape the future of AI data quality in a dynamic startup environment.

• Lead development of systems to evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows
• Define quality standards, metrics, experiments, and processes for assessing agent outputs
• Develop scalable synthetic data validation methods, including failure analysis, task mutation checks, and trajectory audits
• Partner with research engineers, domain experts, and data vendors to diagnose issues and improve data generation workflows
• Turn research insights into production tools, dashboards, validation pipelines, and feedback loops
• Mentor research engineers and promote technical rigor and clear communication