Learning Designer $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 an academic leader who prefers building infrastructure over critiquing it. You possess sufficient subject expertise to recognize high-quality learning, enough analytical rigor to translate that understanding into measurable rubrics and AI-enabled quality controls, and the operational discipline to respond quickly to incomplete data. Your focus is on measurable student progress, and you accept accountability for those results.
2 Hour Learning pursues objectives that most software and education organizations avoid. The model eliminates teachers, textbooks, and the safety net of conventional pedagogy. AI serves as the foundational operating structure. In this capacity, you employ it to produce and refine learning materials, architect interventions, track quality standards, and advance platform capabilities. The operational tempo resembles high-performance technology, consulting, or finance rather than traditional education, with the reward being direct responsibility for subject-level student performance across multiple campuses.
This position centers on operating a function. You will construct AI-driven enhancements to the learning ecosystem, deploy targeted responses to student performance indicators, produce evidence-supported decision documentation, and lead a team measured against explicit benchmarks tied to MAP, AP, SAT, and ISEE outcomes. You will collaborate closely with product, engineering, and data science teams to determine platform priorities. This role suits someone who instinctively adopts AI, conducts independent analysis, delivers under uncertainty, and assumes full ownership of outcomes. It will not suit anyone who values consensus-driven workflows, minimal accountability, or reliance on established systems.
You will function as a central leader within the academics organization, wielding meaningful influence over subject performance and broader learning system evolution. Your colleagues will depend on your standards, your professional judgment, and your capacity to translate student data into actionable strategy. If that prospect feels motivating rather than overwhelming, this role is likely aligned with your strengths.
What you will be doing
- Learning Ecosystem Enhancements — AI-produced refinements to subject-specific K–12 learning experiences spanning content, adaptive pathways, and student interventions, guided by student feedback, platform analytics, assessments, and coaching observations.
- Data-Driven Academic Interventions — Structured intervention strategies for underperforming students or groups, informed by MAP, AP, SAT, ISEE, and comparable performance metrics.
- Student Performance Decision Records — Scalable, evidence-backed documentation of actions taken to improve student outcomes, supported by dashboards, analytics, ticketing systems, surveys, coaching interactions, and assessment results.
- Learning Ecosystem Improvement Specs — Actionable specifications for product, engineering, and data science teams, containing problem definitions, corroborating evidence, anticipated student impact, and acceptance criteria.
What you will NOT be doing
- Repackaging traditional education in an AI wrapper. This is not about translating classroom instruction to digital screens – we are fundamentally rethinking learning from first principles.
- Analyzing data in isolation. Regular interaction with K-12 students is expected, with their feedback treated as critical input from our paying customers.
- Waiting for consensus to push boundaries. You will advocate for an ambitious vision and mobilize others through data-supported results.
- Sticking to conventional methods. You will have latitude to test novel approaches to motivation, assessment, and instruction.
- Fearing AI's impact on education. In this environment, you will leverage AI as a powerful instrument to transform learning, not as a risk requiring mitigation.
Key responsibilities
Lead innovation in AI-powered, teacher-less education to achieve outstanding student outcomes across multiple campuses. Integrate data analytics with consistent student engagement to iteratively refine our learning ecosystem, with success measured by AP exam performance 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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