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Learning Commons aims to scale proven teaching and learning practices to benefit every learner by building AI infrastructure that better connects the way students learn to the tools they learn with. The Team At Learning Commons, we operate at the intersection of technology, research, and philanthropy. We pair product development with grantmaking to scale proven teaching and learning practices for the benefit of every learner. We aim to bring learning science into the tools educators and students use every day. Our work is grounded in a deep belief: when technology reflects the realities of classrooms and the science of how students learn, it can meaningfully strengthen teaching and unlock new possibilities for students. The rise of generative AI offers us a once-in-a-generation opportunity to dramatically accelerate the translation of research insights into practical, classroom-ready tools; tools that honor teachers’ expertise, adapt to students’ needs, and make effective learning practices easier to access, implement, and sustain. In today’s fragmented edtech landscape, school districts are often left piecing together products that don’t always align with curricula or instructional needs. While AI holds enormous potential to support teachers and students, it can only deliver on that promise when grounded in research, high-quality educational data, and expert evaluation. That’s why we’re building open, public-purpose infrastructure — datasets, rubrics, and resources — that help raise the standard for educational tools and create more consistent, impactful learning experiences for all students and teachers. The Opportunity Learning Commons aims to scale proven learning science practices through AI-powered tools, datasets, and evaluation frameworks. As part of the Evaluators team, you will play a critical role in ensuring that AI and education products are grounded in rigorous, research-backed evaluation. You will define and operationalize evaluation frameworks for AI-enabled learning tools, develop metrics and methodologies to assess quality and impact, and generate insights that inform product, research, and ecosystem decisions. This includes evaluating model performance, alignment to pedagogy, and real-world effectiveness in classrooms. You will partner closely with Product, Engineering, Learning Science, and external researchers to ensure that evaluation is embedded throughout the product lifecycle—from early experimentation to scaled deployment. This role sits at the intersection of data science, learning science, and AI system evaluation. What You'll Do Define evaluation frameworks for AI-powered education tools (e.g., LLM-based systems, adaptive learning systems) Design and analyze experiments across structured and unstructured data (A/B testing, quasi-experimental methods, causal inference) Translate findings into clear recommendations for product and research partners Collaborate cross-functionally with Product, Engineering, Learning Science, and external partners Contribute to best practices for responsible AI evaluation, including bias, fairness, and reliability What You'll Bring 5+ years of experience in data science, applied research, or quantitative analysis Proficiency in the modern data science tech stack, including Python, SQL, ML Familiarity with evaluation of generative AI systems (e.g., rubric-based evaluation, human-in-the-loop evaluation) Ability to communicate complex findings to technical and non-technical audiences Experience collaborating with cross-functional teams (product managers, engineers, researchers) in a fast-paced development environment Compensation The Redwood City, CA base pay range for a new hire in this role is $190,000 - $261,800. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and exp