AI-Driven Math Learning Strategist $100,000 USD/year
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Description
- Remote, full-time
- $100,000 USD/year, paid at $50 USD/hour
- Math, AI, and learning science work for K-12 students
If you think students fail math because they are not trying hard enough, this is not the role for you. Here, weak learning is treated as a design problem. Your job is to find where students get stuck, use AI to rebuild the curriculum, assessments, scaffolds, and interventions around that gap, and prove the fix through better mastery data.
2 Hour Learning helps students learn core academics in a fraction of the normal school day. In math, that means using student performance data, platform data, assessment results, and student work to pinpoint the real failure point: a missing scaffold, a weak explanation, poor sequencing, a bad distractor, or an intervention that does not reach the misconception.
This is hands-on work. You will use LLMs, AI-assisted content generation, no-code workflows, and agentic AI tools to redesign explanations, practice sequences, rubrics, answer explanations, and assessments. The reward is direct: thousands of students mastering math faster because the learning system got better, not because someone wrote another polished lesson plan.
If you want to use AI every day to make math learning measurably stronger, apply now.
What you will be doing
- Diagnose recurring math mastery gaps by reviewing performance data, platform data, assessments, student work, and known misconceptions.
- Identify whether the root cause is weak content, poor sequencing, missing scaffolding, bad assessment design, or an ineffective intervention.
- Use LLMs, no-code workflows, and agentic AI tools to redesign math explanations, practice sequences, scaffolds, and interventions.
- Improve math questions, answer explanations, distractors, rubrics, and supporting content so they measure mastery and expose misconceptions.
- Validate AI-generated or AI-modified materials for mathematical accuracy, grade fit, clarity, rigor, and alignment with Alpha's learning science standards.
What you will NOT be doing
- Writing one-off lesson plans that look polished but never move mastery data.
- Stopping at analysis. You will not only find the learning gap; you will rebuild the material that closes it.
- 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.
- Designing broad academic strategy for an entire subject. This is deep hands-on improvement of defined grade-level materials.
Key responsibilities
Accelerate K-12 math learning outcomes by continuously improving curriculum, assessments, scaffolds, and interventions until students master math faster.
Candidate requirements
- 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.
- Strong K-12 math subject expertise, including accuracy, rigor, sequencing, misconceptions, and grade-level expectations.
- Practical knowledge of learning science or instructional design, such as mastery learning, direct instruction, scaffolding, cognitive load, or retrieval 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 learning gaps and improve curriculum, assessments, scaffolding, or interventions.
- Excellent written communication skills, especially explaining complex math concepts clearly.
Nice to have
- Direct 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.
Applying for a role? Here’s what to expect.
Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.
Chat-style screening interview.
Cognitive aptitude test.
Prove real-world job skills.
Interview with the hiring manager.
Pass proctored test.
Accept job offer.
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