Senior Applied Scientist AI Evaluation & Quality Systems

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 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 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

Design and implement scalable ground truth generation pipelines across varied task types annotation modalities and cold start conditionsnBuild and maintain calibration frameworks that keep LLM evaluators anchored to human judgment over timenDevelop anomaly detection systems that surface evaluator drift distribution shifts and coverage gaps across human annotation and automated evaluation pipelinesnDesign build and deploy autonomous QA agents targeting specific facets of evaluation quality architected for generalizability and self-service adoption across teamsnPartner closely with cross-functional teams to ensure evaluation systems meet the highest standards of accuracy consistency and relevancenCommunicate findings and recommendations clearly to both technical and non-technical stakeholders including senior leadershipnContribute to a culture of technical excellence by sharing knowledge and best practices across the team

5 years of industry experience in applied science or machine learning with demonstrated impact on shipped systemsnStrong hands-on experience with Large Language Models including prompt engineering and applied use cases such as grading validation or classificationnStrong working knowledge of evaluation methodology for generative AI including LLM-as-a-judge design meta-evaluation and failure mode analysisnFamiliarity with human-in-the-loop evaluation systems and the operational dynamics that affect data quality at scalenHands-on experience designing ground truth generation pipelines across varied task types and annotation modalitiesnProficiency in Python and relevant ML frameworks with production experience building deploying and monitoring LLM-based pipelines and agentsnMS 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 fieldnExperience designing agent architectures that are configurable and extensible by practitioners who did not build themnHands-on experience building anomaly detection systems for evaluation quality including drift detection distribution analysis and systematic bias identificationnStrong communication skills with the ability to influence technical direction across cross-functional teamsnDemonstrated passion for leveraging AI to improve work efficiency and scale