What are the responsibilities and job description for the AI Experience Researcher, Product Evaluation, Vision Products Group position at Apple, Inc.?
We are seeking a highly motivated and analytical AI Experience Researcher to join our team. This role blends cognitive and human sciences, data sciences, systems design, and product evaluation to ensure AI-powered products deliver exceptional and intuitive customer experiences.\\nYou will work alongside a small but impactful team, collaborating with ML and data scientists, software engineers, designers, project managers, and other cross-functional teams at Apple to define success criteria for AI experiences, and create rigorous evaluations that measure these criteria in iterative product development cycles. If you're passionate about applying scientific rigor to real-world problems, thrive on innovation, and want your work to impact hundreds of millions of users, this role offers an exceptional opportunity to make a lasting contribution to products people use every day.
The central challenge of this role is figuring out what \"good\" means for an AI experience, and then designing rigorous evaluations that measure those qualities reliably and at scale. This requires both deep theoretical grounding in human experience and a solid analytical mindset to operationalize that understanding into scalable evaluation frameworks.\nLeaning on research in human sciences, you will decompose complex AI interactions into their constituent parts, reason about how those parts interact, and build evaluation frameworks that hold up under the scrutiny of non-deterministic nature of AI experiences and the pressures of iterative product development. You will derive experimental designs, create golden data sets, write tests, and turn them into prompts for LLM judges or instructions for human raters. You will run automated evaluations, analyze results, and present findings to diverse stakeholders. \n\nCandidates who bring both quantitative rigor and a qualitative sensibility - to recognize patterns in model behaviors and outputs, and to develop an interpretive understanding of what the data is and isn't capturing from a human perspective - will thrive in this role.What matters most is the ability to hold both orientations at once - to think carefully about what makes an experience work, and to measure complex human dimensions with precision. We are also looking for someone who is excited to co-create what this discipline looks like going forward - bringing intellectual curiosity and a point of view about where human-centered AI evaluation should be headed.
Advanced degree in Cognitive Psychology, Human-Computer Interaction (HCI), User Experience (UX) Research, Learning Sciences, Learning Analytics, Psychometrics, Applied Behavioral Science, or a related field with a focus on human cognition, behavior, and empirical evaluation \nA strong data-driven mindset with experience designing and conducting rigorous empirical research or evaluation - including experimental design, data analysis, and interpretation of various qualitative and quantitative data - particularly in the context of complex human-system interactions \nAbility to reason from theoretical grounding about what makes an experience good in a given context, and to translate that reasoning into evaluation frameworks and measurement designs \nDemonstrated ability to operationalize research literature, qualitative user feedback, and quantitative behavioral data into actionable evaluation criteria, observable metrics, and product insights \nProficiency in data analysis and interpretation, with a strong understanding of statistical validity in evaluation contexts \nExceptional collaboration skills with a track record of working effectively in cross-functional teams that include engineering, ML, design, QA, leadership, and subject matter experts of diverse domains\nStrong communication skills, with the ability to translate complex research findings and evaluation results into clear, actionable recommendations for both technical and non-technical audiences
Familiarity with methods for capturing experiential quality beyond task success - such as cognitive interviews, think-aloud protocols, interaction analysis, or discourse and conversation analysis\nExperience designing and implementing automated evaluation pipelines, including writing prompts for LLM judges and constructing human-in-the-loop or multi-turn evaluation setups\nExperience working with multimodal or agentic systems, AI/ML models, preferably Large Language Models\nFamiliarity with automated testing frameworks and tooling\nExperience with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage\nPortfolio demonstrating previous evaluation frameworks, research findings, or measurable contributions to product improvement\nBackground in learning sciences or instructional design, with experience reasoning about what makes a complex human experience effective is a plus
The central challenge of this role is figuring out what \"good\" means for an AI experience, and then designing rigorous evaluations that measure those qualities reliably and at scale. This requires both deep theoretical grounding in human experience and a solid analytical mindset to operationalize that understanding into scalable evaluation frameworks.\nLeaning on research in human sciences, you will decompose complex AI interactions into their constituent parts, reason about how those parts interact, and build evaluation frameworks that hold up under the scrutiny of non-deterministic nature of AI experiences and the pressures of iterative product development. You will derive experimental designs, create golden data sets, write tests, and turn them into prompts for LLM judges or instructions for human raters. You will run automated evaluations, analyze results, and present findings to diverse stakeholders. \n\nCandidates who bring both quantitative rigor and a qualitative sensibility - to recognize patterns in model behaviors and outputs, and to develop an interpretive understanding of what the data is and isn't capturing from a human perspective - will thrive in this role.What matters most is the ability to hold both orientations at once - to think carefully about what makes an experience work, and to measure complex human dimensions with precision. We are also looking for someone who is excited to co-create what this discipline looks like going forward - bringing intellectual curiosity and a point of view about where human-centered AI evaluation should be headed.
Advanced degree in Cognitive Psychology, Human-Computer Interaction (HCI), User Experience (UX) Research, Learning Sciences, Learning Analytics, Psychometrics, Applied Behavioral Science, or a related field with a focus on human cognition, behavior, and empirical evaluation \nA strong data-driven mindset with experience designing and conducting rigorous empirical research or evaluation - including experimental design, data analysis, and interpretation of various qualitative and quantitative data - particularly in the context of complex human-system interactions \nAbility to reason from theoretical grounding about what makes an experience good in a given context, and to translate that reasoning into evaluation frameworks and measurement designs \nDemonstrated ability to operationalize research literature, qualitative user feedback, and quantitative behavioral data into actionable evaluation criteria, observable metrics, and product insights \nProficiency in data analysis and interpretation, with a strong understanding of statistical validity in evaluation contexts \nExceptional collaboration skills with a track record of working effectively in cross-functional teams that include engineering, ML, design, QA, leadership, and subject matter experts of diverse domains\nStrong communication skills, with the ability to translate complex research findings and evaluation results into clear, actionable recommendations for both technical and non-technical audiences
Familiarity with methods for capturing experiential quality beyond task success - such as cognitive interviews, think-aloud protocols, interaction analysis, or discourse and conversation analysis\nExperience designing and implementing automated evaluation pipelines, including writing prompts for LLM judges and constructing human-in-the-loop or multi-turn evaluation setups\nExperience working with multimodal or agentic systems, AI/ML models, preferably Large Language Models\nFamiliarity with automated testing frameworks and tooling\nExperience with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage\nPortfolio demonstrating previous evaluation frameworks, research findings, or measurable contributions to product improvement\nBackground in learning sciences or instructional design, with experience reasoning about what makes a complex human experience effective is a plus