Lead Research Engineer, Data Quality

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Descrizione

About the Role Lead the data quality team at an AI and machine learning company focused on reinforcement learning and agent evaluation. You will shape the strategy and systems used to measure and improve training data, helping ensure it is useful for training agents on real-world tasks. What You'll Do Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and specialized workflows. Define data quality strategy, establish standards, and design experiments to assess agent outputs. Develop scalable methods to validate synthetic data, including failure analysis, task mutation checks, and trajectory audits. Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows. Turn research insights into production tools, dashboards, validation pipelines, and feedback loops. Develop shared standards for identifying training data that is useful beyond superficial correctness. Mentor research engineers and promote technical rigor, clear communication, and effective execution. What We're Looking For At least 5 years of research or engineering experience, including building AI or machine learning data evaluation systems. Experience leading technical projects or teams through ambiguous problems, from definition to implementation and iteration. Advanced proficiency with Python, Docker, and Linux. Background in AI research, evaluation, post-training, synthetic data,

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