What are the responsibilities and job description for the Senior Applied Scientist - AI Evaluation & Quality Systems position at Apple, Inc.?
Apple Services Engineering (ASE) powers the AI and LLM features behind experiences that hundreds of millions of users love every day. As these systems increasingly rely on human-in-the-loop evaluation, the quality of our products is directly constrained by the quality of our evaluation systems. We believe that to build exceptional AI, you need exceptional mechanisms to validate the signals used to train and evaluate them.
The Human-centered AI, Data Quality Operations team is looking for a Senior Applied Scientist to join our growing team. We are building the systems and methodologies that make AI evaluation trustworthy, and scalable - directly shaping how Apple develops and validates AI across products and services. In this role, you will develop novel, scalable quality control solutions, working closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance.\n\nYour work will span two connected problem spaces. The first is the methodology and tooling that generates reliable ground truth and detects quality failures across human annotation and automated evaluation pipelines. The second is the autonomous QA agents that make those methodologies generalizable across teams and use cases. This role demands fluency across research thinking and engineering execution - you will prototype, validate, and ship. A strong point of view on when not to use a model or agent is as valued here as the ability to build one.\n
5 years of industry experience in applied science or machine learning with demonstrated impact on shipped systems\nStrong hands-on experience with Large Language Models including prompt engineering and applied use cases such as grading, validation, or classification\nStrong working knowledge of evaluation methodology for generative AI, including LLM-as-a-judge design, meta-evaluation, and failure mode analysis\nFamiliarity with human-in-the-loop evaluation systems and the operational dynamics that affect data quality at scale\nHands-on experience designing ground truth generation pipelines across varied task types and annotation modalities\nProficiency in Python and relevant ML frameworks, with production experience building, deploying, and monitoring LLM-based pipelines and agents\nMS or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience
PhD in Computer Science, Machine Learning, Statistics, or a related field\nExperience designing agent architectures that are configurable and extensible by practitioners who did not build them\nHands-on experience building anomaly detection systems for evaluation quality, including drift detection, distribution analysis, and systematic bias identification\nStrong communication skills with the ability to influence technical direction across cross-functional teams\nDemonstrated passion for leveraging AI to improve work efficiency and scale
The Human-centered AI, Data Quality Operations team is looking for a Senior Applied Scientist to join our growing team. We are building the systems and methodologies that make AI evaluation trustworthy, and scalable - directly shaping how Apple develops and validates AI across products and services. In this role, you will develop novel, scalable quality control solutions, working closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance.\n\nYour work will span two connected problem spaces. The first is the methodology and tooling that generates reliable ground truth and detects quality failures across human annotation and automated evaluation pipelines. The second is the autonomous QA agents that make those methodologies generalizable across teams and use cases. This role demands fluency across research thinking and engineering execution - you will prototype, validate, and ship. A strong point of view on when not to use a model or agent is as valued here as the ability to build one.\n
5 years of industry experience in applied science or machine learning with demonstrated impact on shipped systems\nStrong hands-on experience with Large Language Models including prompt engineering and applied use cases such as grading, validation, or classification\nStrong working knowledge of evaluation methodology for generative AI, including LLM-as-a-judge design, meta-evaluation, and failure mode analysis\nFamiliarity with human-in-the-loop evaluation systems and the operational dynamics that affect data quality at scale\nHands-on experience designing ground truth generation pipelines across varied task types and annotation modalities\nProficiency in Python and relevant ML frameworks, with production experience building, deploying, and monitoring LLM-based pipelines and agents\nMS or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience
PhD in Computer Science, Machine Learning, Statistics, or a related field\nExperience designing agent architectures that are configurable and extensible by practitioners who did not build them\nHands-on experience building anomaly detection systems for evaluation quality, including drift detection, distribution analysis, and systematic bias identification\nStrong communication skills with the ability to influence technical direction across cross-functional teams\nDemonstrated passion for leveraging AI to improve work efficiency and scale