AI-Driven Science 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
Not accepting applications on crossover.com at this time.
Description
- Remote
- $100,000 USD/year
- Full-time
Most science curriculum roles stop at building lessons and assessments. This one goes further: you own whether students master science faster because the learning system got better.
At 2 Hour Learning, students use AI-powered learning systems. Your job is to find where science learning breaks down, then redesign the curriculum, scaffolds, practice, assessments, and interventions that fix it. Growth on measures like MAP, AP, and SAT is the scoreboard, not the polish of the materials.
A normal week may include reviewing student work and platform data, spotting a recurring misconception in chemistry or physics, using AI tools to rebuild an explanation or practice sequence, then checking whether the new path helps students master the concept. You will use large language models (LLMs), AI-assisted workflows, learning science, and deep K-12 science knowledge to turn evidence into better learning materials.
If you believe weak learning outcomes are design problems to solve, not excuses to accept, apply today.
What you will be doing
- Diagnose recurring K-12 science mastery gaps using student performance data, platform signals, assessment results, and student work.
- Separate root-cause misconceptions and reasoning breakdowns from surface mistakes or low engagement.
- Use AI tools, LLMs, no-code workflows, or coding/API-based tools to redesign science explanations, models, scaffolds, practice sequences, and interventions.
- Improve science assessments, questions, answer explanations, distractors, rubrics, and content so they measure mastery and surface misconceptions.
- Validate AI-generated or AI-modified science materials for accuracy, clarity, rigor, grade-level fit, and alignment with Alpha’s learning science standards.
What you will NOT be doing
- Blaming students when they do not learn science. You improve the learning system instead.
- Writing polished lessons that are never tested against student evidence. The work must connect to mastery data.
- Doing broad academic strategy while someone else fixes the materials. This is hands-on redesign work.
- Building education software products from scratch. You will shape learning materials and AI workflows, not own the product stack.
- Staying in traditional curriculum design without AI as a daily work tool.
Key responsibilities
Accelerate science learning outcomes by continuously improving curriculum, scaffolding, interventions, content, and assessments using AI, learning science, and student evidence.
Candidate requirements
- Bachelor's degree or higher in Science, Science Education, Biology, Chemistry, Physics, Earth Science, Environmental Science, Cognitive Science, Instructional Design, or a closely related field.
- 3+ years of experience in science instruction, science curriculum development, assessment development, educational content creation, or learning design.
- Strong K-12 science subject knowledge, including scientific accuracy, conceptual sequencing, common misconceptions, and grade-level expectations.
- Practical knowledge of learning science or instructional design, such as mastery learning, scaffolding, direct instruction, cognitive load, or retrieval practice.
- Experience using AI tools such as ChatGPT, Claude, Copilot, no-code workflows, agentic AI tools, or coding/API-based tools to improve educational work.
- Ability to use student learning data, assessment results, and student work to identify science learning gaps and improve curriculum, assessment, scaffolding, or intervention design.
- Strong written communication skills, especially explaining complex science concepts clearly.
Nice to have
- Direct experience with large language model (LLM) APIs.
- Experience in EdTech, adaptive learning, mastery-based learning, or AI-powered education products.
- Experience sharing science education, AI, or learning science insights with an online community.
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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