Lead Research Engineer, Data Quality

• 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.
• 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.
• On-site in the United States.

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