This role is designed for academic leaders who prefer to construct systems rather than critique them. You should possess subject mastery to recognize high-quality learning, the rigor to translate that understanding into rubrics and AI-driven quality mechanisms, and the pragmatism to respond decisively to incomplete data. Student improvement matters to you, and you are prepared to be held accountable for that metric. 2 Hour Learning pursues an approach that the broader software and education industries seldom undertake. There are no instructors, no traditional textbooks, and no conventional teaching layer supporting the product. AI serves as the core operating framework. In this position, you will leverage it to produce and refine learning materials, architect interventions, oversee quality assurance, and advance the platform. The operational tempo resembles high-performance technology, consulting, or finance more than traditional education, and the reward is direct authority over student outcomes in your subject area across multiple campuses. This position centers on functional leadership. You will develop AI-driven enhancements to the learning ecosystem, address student performance indicators through focused interventions, produce evidence-backed decision documentation, and lead a team measured against concrete standards linked to MAP, AP, SAT, and ISEE outcomes. You will also collaborate extensively with product, engineering, and data science teams to determine platform priorities. This role suits someone who instinctively turns to AI, conducts independent analysis, executes under uncertainty, and accepts complete ownership of results. It will not suit anyone who favors consensus-driven processes, minimal accountability, or the security of established systems. What You Will Be Doing Learning Ecosystem Enhancements — AI-generated refinements to subject-specific K–12 learning experiences encompassing content, adaptive pathways, and student interventions, shaped by student feedback, analytics, assessments, and coaching insights. Data-Driven Academic Interventions — Focused intervention strategies for underperforming students or cohorts, informed by MAP, AP, SAT, ISEE, and comparable performance metrics. Student Performance Decision Records — Systematic, evidence-grounded decisions that document actions taken to improve student performance, reinforced by dashboards, analytics, tickets, surveys, coaching calls, and assessment data. Learning Ecosystem Improvement Specs — Execution-ready specifications for product, engineering, and data science enhancements, featuring problem definitions, corroborating evidence, anticipated student impact, and acceptance criteria. What You Won’t Be Doing Repackaging traditional education in an AI wrapper. This isn't about replicating classroom instruction via screens – we're fundamentally reimagining learning from the ground up. Analyzing data in isolation. You'll be expected to regularly engage with K‑12 students, valuing their feedback as essential input from our paying customers. Waiting for consensus to push boundaries. You'll champion a bold vision and rally others around data-driven results. Sticking to conventional methods. You'll be free to experiment with innovative approaches to motivation, assessment, and instruction. Fearing AI's impact on education. Here, you'll harness AI as an exciting tool to revolutionize learning, not as a threat to be mitigated. Vice President Learning Key Responsibilities Lead innovation in AI-powered, teacher-less education to achieve outstanding student outcomes across multiple campuses. Integrate data analytics with consistent student interaction to continually refine our learning ecosystem, as evidenced by AP exam results and MAP assessment growth. Basic Requirements 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. Compensation and Benefits This is a full-time (40 hours per week), long-term position. The position is immediately available and requires entering into an independent contractor agreement with Crossover as a Contractor of Record. The compensation level for this role is $100 USD/hour, which equates to $200,000 USD/year assuming 40 hours per week and 50 weeks per year. The payment period is weekly. #J-18808-Ljbffr