What are the responsibilities and job description for the Measurement Scientist, AI Evaluation Platform position at Apple, Inc.?
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or App Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.
Our team, part of Apple Services Engineering, is building the scientific foundation for how AI systems are evaluated across Apple. We are seeking a Measurement Scientist to ensure that our evaluation methods are not just sophisticated, but scientifically valid and trustworthy . In this role, you will apply psychometric theory , validity frameworks, and statistical rigor to establish measurement standards for AI evaluation - ensuring that when we claim an evaluator measures \"helpfulness\" or \"safety ,\" it actually does. We are looking for individuals across a range of experience levels. \nThis role uniquely bridges measurement science and cutting-edge AI evaluation. You will develop methods for validating LLM-as-judge evaluators, automated benchmarks, and human evaluations. And you will create statistical tools that help engineers trust their evaluation results. You will work on an interdisciplinary team with ML researchers to solve new problems in AI evaluation. Your work will be both published at top measurement and ML venues and productionized into the evaluation SDK used across Apple. \nThe successful candidate will have deep expertise in psychometrics and measurement theory , with the ability to apply these principles to novel AI evaluation challenges. You will work collaboratively with ML researchers, platform engineers, and evaluation practitioners to translate measurement science into practical tools that scale across the organization.
PhD in Psychometrics, Educational Measurement, Quantitative Psychology , Statistics, or equivalent research/work experience.\nDeep expertise in modeling test data (IRT or related methods) and construct validation.\nStrong statistical foundation including experimental design, power analysis, sampling theory , and uncertaintyquantification.\nTrack record of designing and validating measurement instruments as demonstrated through publications or applied work.\nProficiency in Python (preferred) or R for statistical analysis, psychometric modeling, and method implementation.\nStrong working knowledge of generative AI technology\nExcellent communication skills with the ability to explain complex measurement concepts to engineers, ML researchers, and non-technical stakeholders.
Experience applying measurement science to AI/ML evaluation, automated scoring systems, or computational assessment.\nKnowledge of modern ML evaluation challenges including LLM-as-judge, automated metrics, benchmark design, and agentic systems.\nPublications at measurement venues or top ML conferences (NeurIPS, ICML, ICLR).\nExpertise in computational social or behavioral science using generative AI\nExperience collaborating with engineers to turn research methods into production tools and scalable infrastructure.
Our team, part of Apple Services Engineering, is building the scientific foundation for how AI systems are evaluated across Apple. We are seeking a Measurement Scientist to ensure that our evaluation methods are not just sophisticated, but scientifically valid and trustworthy . In this role, you will apply psychometric theory , validity frameworks, and statistical rigor to establish measurement standards for AI evaluation - ensuring that when we claim an evaluator measures \"helpfulness\" or \"safety ,\" it actually does. We are looking for individuals across a range of experience levels. \nThis role uniquely bridges measurement science and cutting-edge AI evaluation. You will develop methods for validating LLM-as-judge evaluators, automated benchmarks, and human evaluations. And you will create statistical tools that help engineers trust their evaluation results. You will work on an interdisciplinary team with ML researchers to solve new problems in AI evaluation. Your work will be both published at top measurement and ML venues and productionized into the evaluation SDK used across Apple. \nThe successful candidate will have deep expertise in psychometrics and measurement theory , with the ability to apply these principles to novel AI evaluation challenges. You will work collaboratively with ML researchers, platform engineers, and evaluation practitioners to translate measurement science into practical tools that scale across the organization.
PhD in Psychometrics, Educational Measurement, Quantitative Psychology , Statistics, or equivalent research/work experience.\nDeep expertise in modeling test data (IRT or related methods) and construct validation.\nStrong statistical foundation including experimental design, power analysis, sampling theory , and uncertaintyquantification.\nTrack record of designing and validating measurement instruments as demonstrated through publications or applied work.\nProficiency in Python (preferred) or R for statistical analysis, psychometric modeling, and method implementation.\nStrong working knowledge of generative AI technology\nExcellent communication skills with the ability to explain complex measurement concepts to engineers, ML researchers, and non-technical stakeholders.
Experience applying measurement science to AI/ML evaluation, automated scoring systems, or computational assessment.\nKnowledge of modern ML evaluation challenges including LLM-as-judge, automated metrics, benchmark design, and agentic systems.\nPublications at measurement venues or top ML conferences (NeurIPS, ICML, ICLR).\nExpertise in computational social or behavioral science using generative AI\nExperience collaborating with engineers to turn research methods into production tools and scalable infrastructure.