Head of Learning $200,000 USD/year Pay is set based on global value, not the local market.
Most roles = hourly rate x 40 hrs x 50 weeks
Description
This position is designed for academic leaders who prefer to engineer learning systems rather than critique them from the outside. You bring sufficient subject-matter expertise to recognize excellence in educational outcomes, enough precision to codify that expertise into rubrics and AI-driven quality mechanisms, and the pragmatism to respond to imperfect data quickly. Student growth is what matters to you, and you are prepared to be measured against that standard.
2 Hour Learning is attempting what most software and education organizations avoid. Teachers, textbooks, and the familiar scaffolding of traditional pedagogy are absent from the product. AI forms the core operating model. In this capacity, you deploy it to produce and refine learning materials, architect interventions, oversee quality assurance, and advance platform capabilities. The velocity resembles high-performance technology, consulting, or financial sectors more than traditional education, and the reward is direct accountability for subject-level student performance across multiple sites.
This position centers on operational leadership. You will develop AI-powered enhancements to the learning ecosystem, design targeted responses to student performance data, document evidence-based decisions, and guide a team measured by MAP, AP, SAT, and ISEE outcomes. You will collaborate closely with product, engineering, and data science to shape platform priorities. This role suits individuals who default to AI, independently handle analysis, deliver under uncertainty, and accept complete accountability. It will not suit those who favor consensus-driven timelines, minimal oversight, or reliance on established frameworks.
You will serve as a senior leader within the academics team, wielding significant influence over subject performance and the evolution of the broader learning system. The team will depend on your standards, judgment, and capacity to translate student data into intervention. If that prospect energizes you rather than overwhelms you, this role is likely a strong match.
What you will be doing
- Learning Ecosystem Enhancements — AI-driven improvements to K–12 subject-specific learning experiences, spanning content development, adaptive learning pathways, and student interventions, shaped by student feedback, performance analytics, assessments, and coaching observations.
- Data-Driven Academic Interventions — Focused intervention strategies for underperforming students or groups, informed by MAP, AP, SAT, ISEE, and other performance metrics.
- Student Performance Decision Records — Structured, evidence-backed decision documentation capturing student performance improvement initiatives, supported by dashboards, analytics, ticketing systems, surveys, coaching sessions, and assessment results.
- Learning Ecosystem Improvement Specs — Actionable specifications for product, engineering, and data science work, detailing problem definitions, supporting data, projected student impact, and success criteria.
What you will NOT be doing
- Repackaging traditional education in an AI wrapper. This is not about digitizing classroom instruction – we are rethinking learning architecture from first principles.
- Analyzing data in isolation. Regular interaction with K-12 students is expected, treating their feedback as critical input from our primary customers.
- Waiting for consensus to push boundaries. You will advocate for an ambitious vision and mobilize others through data-backed outcomes.
- Sticking to conventional methods. You will have latitude to test novel strategies in motivation, assessment, and pedagogy.
- Fearing AI's impact on education. In this environment, you will leverage AI as a transformative instrument for learning innovation, not a risk to be contained.
Key responsibilities
Lead innovation in AI-powered, teacher-independent education to produce outstanding student outcomes across campuses. Integrate data analytics with direct student engagement to iteratively refine the learning ecosystem, evaluated through AP exam results and MAP assessment growth.
Candidate 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
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