Staff Applied Scientist, AI Quality & Meta Evaluation

Apple Services Engineering (ASE) powers AI and LLM features across App Store, Music, Video, and more. As these systems increasingly rely on LLM Judges and automated evaluators to score model performance at scale, the trustworthiness of those evaluation signals becomes mission-critical. We believe that to build exceptional LLMs, you need exceptional mechanisms to validate the signals used to train and evaluate them.

Description

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?"

","responsibilities":"Design, develop, and iterate on the reasoning agent that serves as our adjudicator, auditing Production LLM Judge outputs for hallucination, drift, and systematic bias

Develop the statistical and ML approaches that detect when Production LLM Judges diverge from ground truth, including confidence calibration, entropy-based uncertainty quantification, and out-of-distribution detection

Define the algorithms that determine what gets routed for deeper review, moving the team from random sampling to principled, risk-stratified smart sampling

Design the hierarchical weighting model and the confidence interval framework that replaces misleading point estimates with statistically rigorous ranges

Establish the standards for how immutable ground truth sets are built, versioned, and validated, including inter-annotator agreement protocols

Partner with Autograder Developers to validate new LLM Judge through our standard validation processes, ensuring LLM Judges are rigorously validated before reaching production

Serve as the scientific authority on data quality evaluation methodology for partner teams across ASE, translating complex statistical findings into clear decision-readiness signals for engineering and leadership stakeholders

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

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