Learning Systems Data Engineer

OpenAI OpenAI · AI Frontier · New York, NY · Go To Market

This role focuses on building the infrastructure for AI-native learning experiences, translating pedagogical goals into production systems like learner models, progress tracking, and adaptive feedback loops. The goal is to ensure millions of people can learn effectively with AI tools.

What you'd actually do

  1. Build AI-Native Learning Infrastructure: Develop core systems for AI education, including dynamic experiences, progress tracking, and assessments.
  2. Enable Adaptive Learning: Develop capabilities that allow learning experiences to dynamically adapt to learners’ knowledge, goals, and behaviors over time.
  3. Design Data Systems for Insights: Build data pipelines and analytics systems to help educators understand learner outcomes, engagement patterns, and skill development.
  4. Empower Educators: Build systems that allow non-engineers to design, configure, and experiment with learning experiences without requiring direct engineering support.

Skills

Required

  • software engineering
  • data engineering
  • learning engineering
  • data systems
  • infrastructure
  • education & training systems
  • learning data
  • analytics pipelines
  • educational metrics
  • pedagogical goals translation

Nice to have

  • learning science
  • instructional design
  • education research
  • LMS platforms
  • training infrastructure
  • learning analytics
  • learner data models
  • educational measurement
  • AI models in educational workflows
  • credentialing systems
  • certification systems
  • competency frameworks
  • platforms for non-technical educators

What the JD emphasized

  • 5–10+ years of experience in software, data, or learning engineering
  • Experience building data systems or infrastructure that support education & training
  • Experience working with learning data, analytics pipelines, or educational metrics
  • Comfort translating learning or pedagogical goals into technical systems

Other signals

  • AI-native learning experiences
  • learner models
  • progress tracking
  • adaptive feedback loops
  • formative assessments
  • analytics to measure whether people are actually learning