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AI Spaced Repetition Curriculum Optimizer

John Mueller

Full-time · Job · Chicago

About the role

Our mission is to make language learning stick, not just feel productive in the moment. This role focuses on the spaced repetition engine that decides when each learner sees a vocabulary card again, using their past recall accuracy and response time to personalize review timing. You'll analyze learner review history with pandas, test scheduling model variants with scikit-learn, and measure whether changes actually improve long-term retention rather than just short-term quiz scores. It's a great entry point into applied ML if you care about learning science as much as code.

Responsibilities

  • Analyze learner review history to identify patterns in recall accuracy and timing
  • Test and tune spaced repetition scheduling model variants
  • Measure long-term retention impact of scheduling changes, not just short-term quiz scores
  • Collaborate with curriculum designers to align scheduling logic with learning goals

Requirements

  • Working knowledge of Python and pandas for data analysis
  • Basic scikit-learn experience or strong willingness to learn
  • Interest in learning science and spaced repetition research
  • Comfortable working with large longitudinal behavioral datasets

Benefits

  • Fully remote position
  • Health insurance
  • Free access to all language courses on the platform
  • Structured onboarding and mentorship

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Categories

AI Product & Strategy

Skills

python pandas scikit-learn

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

AI Product & Strategy

Skills

python pandas scikit-learn

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