AI-Driven Fast Math Learning Strategist

$100,000 USD/year ^{Pay is set based on global value, not the local market. Most roles = hourly rate x 40 hrs x 50 weeks}  ($50 USD/hour)

• Diagnose recurring gaps in math fluency, fact mastery, speed, and accuracy using performance data, assessments, platform data, and student work.
• Identify what is preventing automaticity, from weak sequencing and missing scaffolds to ineffective practice, feedback, assessments, or interventions.
• Use LLMs, no-code workflows, and agentic AI tools to redesign practice sequences, explanations, scaffolds, and interventions for Fast Math.
• Improve questions, answer explanations, distractors, rubrics, and assessments so they expose misconceptions and accurately measure mastery.
• Validate AI-generated or AI-modified materials for mathematical accuracy, grade fit, clarity, rigor, and alignment with Alpha's learning science standards.

• Producing repetitive drill exercises without evidence that they improve fluency or automaticity.
• Writing one-off lesson plans that look polished but never move mastery data.
• Stopping at analysis. You will find the learning gap, rebuild the material, and measure whether the change worked.
• Managing a traditional curriculum program, handling admin work, or coordinating a slow review process.
• Building education software products. Your focus is the math learning experience, not the app codebase.

Accelerate K-12 math fluency by continuously improving Fast Math curriculum, practice, assessments, scaffolds, and interventions until students calculate accurately and automatically.

• Bachelor's degree or higher in Mathematics, Statistics, Applied Math, Engineering, Physics, or another quantitative field.
• 3+ years of experience in math instruction, math curriculum development, assessment development, educational content creation, or learning design, especially focused on building math fluency and fact mastery and building students toward automaticity in four-function math.
• Strong K-12 math subject expertise, including accuracy, rigor, sequencing, misconceptions, fluency development, and grade-level expectations.
• Practical knowledge of learning science or instructional design, such as mastery learning, direct instruction, scaffolding, cognitive load, retrieval practice, or deliberate practice.
• Experience using AI tools to improve educational work, such as LLMs, AI-assisted content generation, no-code workflows, coding/API-based tools, or agentic AI tools.
• Ability to use student learning data to identify barriers to fluency and improve curriculum, assessments, practice, scaffolding, or interventions.
• Excellent written communication skills, especially explaining mathematical concepts clearly and precisely.

• Direct experience designing programs or interventions focused on arithmetic fluency, fact mastery, or automaticity.
• Experience with LLM APIs for content generation, data analysis, or adaptive learning work.
• Experience building or improving math assessments that surface misconceptions, not just procedural accuracy.
• Experience sharing education, math, or AI insights in an online community, such as Twitter/X, LinkedIn, or a public knowledge base.