You should want this role if you are the kind of academic leader who would rather build the system than comment on it. You are fluent enough in your subject to know what high-quality learning looks like, rigorous enough to turn that judgment into rubrics and AI-powered quality checks, and practical enough to act on messy data fast. You care about whether students actually improve, and you are comfortable being judged by that outcome. 2 Hour Learning is doing something the rest of the software and education world rarely attempts. There are no teachers, no textbooks, and no comfortable layer of traditional instruction underneath the product. AI is the operating model. In this role, you use it to generate and improve learning content, design interventions, monitor quality, and push the platform forward. What you will be doing: - Learning Ecosystem Enhancements: AI-generated improvements to subject-specific K–12 learning experiences across content, adaptive pathways, and student interventions. - Data-Driven Academic Interventions: Targeted intervention plans for off-track students or cohorts, driven by MAP, AP, SAT, ISEE, and related performance data. - Student Performance Decision Records: Repeatable, evidence-based decisions documenting student performance improvement actions. - Learning Ecosystem Improvement Specs: Implementation-ready specifications for product, engineering, and data science enhancements.
- Master's degree or higher in Educational Science, Learning Science, Psychology, Psychometrics, Instructional Design, or a related field - At least 5 years of experience in academic or EdTech leadership roles, with direct people management responsibility (hiring, performance evaluation, coaching, termination decisions) - Demonstrated experience using AI tools as part of day-to-day professional workflows, and willingness to rely on AI extensively to improve academic and operational outcomes - Strong understanding of learning science principles, such as Cognitive Load Theory and Mayer's Multimedia Principles, and data-driven educational approaches