As a Principal Applied Scientist on the Human Centered AI team at Apple Services Engineering, you will play a crucial role in developing the Data Quality Validation framework that ensures the trustworthiness of evaluation signals for AI and LLM features. This high-impact position requires you to architect and build data science methodologies that underpin validation models. You will focus on designing statistical frameworks for judge reliability and bridging automated evaluations with human ground truth. Your work will directly address the critical question of evaluator trustworthiness in model assessments.

• 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, including inter-rater reliability and correlation analysis
• 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
• PhD in Statistics, Computer Science, Machine Learning, or a related field
• Experience specifically in LLM evaluation science, including autograder validation and judge-as-a-model frameworks
• Hands-on experience with large-scale reasoning 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

• Own the data science methodology underpinning the data quality validation models.
• Design statistical frameworks that govern judge reliability.
• Work hands-on to integrate automated evaluation with human ground truth.
• Address the critical question of evaluator trustworthiness in model assessments.
• Translate rigorous statistical methodology into actionable guidance for engineering and product partners.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.