Staff Applied Scientist, AI Quality & Meta Evaluation

As a Principal Applied Scientist on the Human Centered AI team, you will be the technical engine behind our Data Quality Validation framework. This is a high-impact individual contributor role for a scientist who wants to architect and build — not just advise. You will own the data science methodology underpinning our data quality validation models, design the statistical frameworks that govern judge reliability, and work hands-on to close the loop between automated evaluation and human ground truth. You will be the person who answers the hardest question in our stack: "Can we trust the evaluators that are evaluating our models?" Minimum Qualifications Master's degree in Statistics, Data Science, Machine Learning, Computer Science, or a related quantitative field 8+ years of hands-on experience in applied data science, ML research, or evaluation science Deep expertise in uncertainty quantification and model calibration — including entropy modeling and Bayesian approaches Demonstrated experience building disagreement detection or anomaly detection models in production ML systems Strong command of statistical measurement frameworks — inter-rater reliability, correlation analysis, and statistical process control Proven experience designing or contributing to Human-in-the-Loop (HITL) or active learning pipelines Proficiency in Python for statistical modeling, ML experimentation, and data pipeline development Exceptional ability to translate rigorous statistical methodology into clear, actionable guidance for engineering and product partners Preferred Qualifications PhD in Statistics, Computer Science, Machine Learning, or a related field Experience specifically in LLM evaluation science — including autograder validation, judge-as-a-model frameworks, or RLHF data quality Hands-on experience with large-scale reasoning models (e.g., 70B+ parameter models) used in chain-of-thought evaluation or meta-reasoning contexts Experience defining governance gates or certification pipelines for AI systems in a CI/CD context Familiarity with out-of-distribution detection techniques for identifying input drift in live production systems Track record of publishing or presenting evaluation methodology work internally or externally