Product Evals Engineer, ChatGPT Learning

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 behavior to uncover systematic failure modes, emerging trends, and high-impact opportunities.
• Improve the reliability of automated evaluators through calibration, validation, and ongoing comparison with human judgments.
• Partner with engineering and research teams to translate findings into improvements across retrieval, ranking, model behavior, and product design.
• Build tools that make evaluation results accessible, interpretable, and actionable for teams across the organization.
• Help establish best practices for measuring the quality and learning impact of AI-powered search experiences.

You Might Thrive in This Role If You

• Are a strong software engineer who can design reliable data pipelines, evaluation infrastructure, and internal tools.
• Understand information retrieval concepts and metrics, while recognizing where conventional search metrics may not capture the quality of an AI-generated learning experience.
• Have experience working with human-labeled data, relevance judgments, annotation programs, or model-based evaluation.
• Are comfortable going deep into qualitative and quantitative data to identify subtle issues and systemic patterns.
• Can turn ambiguous product goals-such as trustworthiness or educational usefulness-into clear, testable definitions of quality.
• Communicate technical findings clearly and can influence decisions across research, engineering, product, and nontechnical partners.
• Have a strong intrinsic desire to learn, fill gaps in your knowledge, and share what you discover with others.
• Care deeply about building AI systems that help people learn while remaining accurate, responsible, and worthy of trust.

Nice to Have

• Experience evaluating large language models, retrieval-augmented generation systems, or agentic search products.
• Familiarity with learning science, education technology, assessment design, or educational measurement.
• Experience with causal inference, online experimentation, or analysis of large-scale product telemetry.
• Knowledge of evaluation challenges involving multilingual, multimodal, or age-appropriate learning experiences.
• Experience developing or validating LLM-based graders and other automated evaluation methods.