Product Evals Engineer, ChatGPT Learning

Job description

About the Team The ChatGPT Learning & Education team is building AI-powered experiences that help people learn, teach, and pursue their curiosity. We work across research, engineering, product, design, and education to make ChatGPT a more effective and trustworthy learning companion for students, educators, and lifelong learners. Factuality, personalization, and model response quality are central to this mission. Learners need answers that are relevant, accurate, well-supported, appropriately scoped, and responsive to their level of understanding. Our team develops the evaluation systems and insights needed to understand these qualities, identify gaps, and improve the experiences we deliver. About the Role As a Product Evals Engineer, you will lead the development of systems that evaluate response quality across ChatGPT’s learning experiences. You will define what excellent responses looks like for learners, translate those principles into measurable criteria, and build the infrastructure needed to evaluate performance at scale. You will combine human judgment, automated evaluation, experimentation, and deep analysis to uncover quality issues and accelerate iteration. This work will span retrieval, ranking, answer quality, source selection and attribution, and end-to-end user outcomes. You will partner closely with model measurement team, product engineers, data scientists, and education experts to ensure our evaluation methods reflect both technical quality and genuine learning value. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In This Role, You Will Build and evolve evaluation frameworks for quality, triggering, and information-grounded experiences within ChatGPT Learning. Define quality metrics and evaluation criteria for relevance, factual accuracy, source quality, coverage, freshness, citation support, safety, and educational usefulness. Develop scalable evaluation pipelines that combine expert human judgment, behavioral signals, model-based grading, and deterministic checks. Create representative datasets and benchmarks spanning student, educator, and lifelong-learning use cases. Design experiments that identify quality gaps, measure progress, and help teams make informed product and model decisions. Analyze evaluation results and user behavi…