Lead Research Engineer, Data Quality

About the Role

Lead the data quality team at an AI and machine learning company focused on reinforcement learning environments and model evaluation. You will shape the systems and research practices that make training data useful for real-world AI agents.

What You'll Do

• Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows.
• Set data quality strategy by establishing standards, building quality control systems, and designing experiments to assess agent outputs.
• Develop scalable methods to validate synthetic data, including failure-mode analysis, task mutation checks, and trajectory audits.
• Work with research engineers, domain experts, and data vendors to identify quality issues and improve data generation workflows.
• Translate research insights into production tools, dashboards, validation pipelines, and feedback loops.
• Develop team practices for recognizing training data that is genuinely useful, realistic, and reliable.
• Mentor research engineers and uphold strong standards for technical rigor and execution.

What We're Looking For

• At least 5 years of relevant research or engineering experience, including building AI/ML data evaluation or quality systems.
• Experience leading technical projects or teams from problem definition through implementation and iteration.
• Advanced proficiency in Python, Docker, and Linux environments.
• Background in AI research, evaluation, post-training, benchmarks, synthetic data, or model evaluation infrastructure.
• Strong judgment about training data quality, including whether tasks are realistic, learnable, diverse, reliable, and useful.
• Experience designing metrics, experiments, and quality assurance processes, and translating research into production systems.
• Experience working with subject-matter experts to scale domain judgment into review or data generation workflows.
• Clear written communication, attention to detail, and comfort working independently in an early-stage startup environment.