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AI Vocabulary Acquisition Modeling Engineer

John Mueller

Full-time · Job · London

About the role

Spaced repetition is the best-validated vocabulary acquisition technique in the learning science literature. Most language apps implement it badly: fixed intervals that don't adapt to the learner, word pools that don't account for semantic relationships, and review sessions that don't connect new vocabulary to previously learned words that share morphological roots or semantic fields. Our platform has 4.2 million language learners across 28 language pairs, and we're rebuilding the vocabulary acquisition model from first principles. You'll design models that estimate each learner's memory decay rate per word (faster for visually similar words, slower for words with L1 cognates), optimize review schedules to maximize long-term retention per minute of study time, and build the semantic clustering that introduces new vocabulary in conceptually related groups. The learning science team — two researchers with cognitive science PhDs — will challenge every modeling decision with domain expertise. You need to be comfortable defending your architectural choices with both empirical evidence and theoretical grounding.

Responsibilities

  • Design adaptive vocabulary review scheduling based on per-learner memory decay modeling
  • Build semantic clustering to sequence vocabulary introduction in related conceptual groups
  • Implement inter-word relationship modeling for morphological and semantic connections
  • Develop A/B testing framework for retention outcome comparisons across scheduling strategies
  • Create learner knowledge state models updated continuously from review performance
  • Collaborate with learning science researchers to ground models in cognitive theory

Requirements

  • 3+ years in ML or data science with educational technology or cognitive modeling experience
  • Understanding of spaced repetition systems and memory retention research
  • Strong Python and PyTorch skills
  • Experience with survival analysis or other time-to-event modeling approaches
  • Ability to engage with interdisciplinary researchers across cognitive science and ML

Benefits

  • Fully remote across UK/EU time zones
  • 28 days PTO plus bank holidays
  • £2,500 annual learning budget
  • Private health insurance
  • Pension matching

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Categories

NLP & Prompt Engineering data science & analytics

Skills

python pytorch hugging face sql statistics

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

NLP & Prompt Engineering data science & analytics

Skills

python pytorch hugging face sql statistics pandas

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