We’re seeking a Senior Member of the Core Data Team to lead the evaluation and optimization of large-scale datasets used to train state-of-the-art AI models. In this role, you’ll help define what "high-quality data" means in practice, using statistical, computational, and ML-driven methods to ensure our data is diverse, representative, and high-impact. You’ll work closely with research and engineering teams to improve model performance through better data. This is an ideal role for someone with a PhD in machine learning, CS, or a related applied field who is passionate about the role of data in AI training and excited to advance Protege’s mission to become the ubiquitous platform for AI training data.

Key Responsibilities:

• Design and apply statistical and machine learning methods to curate, filter, and enrich large-scale unstructured datasets
• Develop frameworks to assess data diversity, duplication, and informativeness. Design statistical approaches to de-risk training datasets
• Collaborate with model training teams to identify data bottlenecks and optimize dataset performance. Emphasis on ability to collaborate with large foundational models and smaller startups
• Provide leadership on data quality strategy and shape internal best practices
• Evaluate external datasets for integration, focusing on scalability, quality, and relevance to model performance. Help build data scorecards
• Contribute to research and development of tools that automate data preprocessing and validation

About You:

• PhD or equivalent Master's Degree + 4+ years industry experience in machine learning, economics, mathematics, engineering, computer science, statistics, or a related quantitative field
• Strong understanding of AI model training pipelines, including pre-processing and evaluation
• Experience working with large, unstructured datasets, especially text
• Background in statistical analysis, bias detection, and data validation
• Able to identify high-impact problems and drive independent solutions

Bonus if you have these attributes:

• Experience with synthetic data generation or augmentation strategies
• Publications or open-source contributions in data-centric AI or related areas
• Experience developing evaluation frameworks or performance metrics for training data
• Cross-functional collaboration with product, infrastructure, or partnership teams