Researcher, Education Labs

Anthropic Anthropic · AI Frontier · San Francisco, CA · Technical Education

Researcher focused on understanding and measuring how people learn and develop AI fluency, designing studies, building evaluation instruments, and translating insights into product improvements and action research. The role emphasizes creating new measures and tools for AI capability growth.

What you'd actually do

  1. Design and run mixed-methods studies on how people develop real skill with AI, measuring success by capability growth rather than engagement.
  2. Build and validate the instruments, measures, and evaluation methods the team relies on, so that findings hold up to scrutiny and can be trusted by research, product and policy partners.
  3. Translate research insights into shipped product, curriculum, and model-level improvements through close collaboration with engineers, designers, and researchers.
  4. Generate net-new insights about how AI is reshaping learning, and how communities and organizations can organize to learn alongside it.
  5. Communicate your work through clear writing, prototypes, and presentations that shape thinking across the organization.

Skills

Required

  • research background in learning sciences, education, cognitive science, HCI, educational psychology, or a closely related field
  • Strong mixed-methods skills: experimental design, measurement and psychometrics, qualitative methods
  • Hands-on technical skill in Python, data analysis, and working with LLMs
  • Comfort deriving insight from imperfect, dynamically changing data
  • Comfort with ambiguity and undefined problem spaces
  • Clear communication and a track record of cross-functional collaboration

Nice to have

  • Experience measuring capability or skill development in production, including experimentation frameworks and A/B testing.
  • Experience building simple tools or interfaces that let non-technical collaborators evaluate or learn from AI systems.
  • Published writing, talks, or open work on skill development, human-AI interaction, or the learning sciences.
  • Experience in learning platforms, developer tools, creative tools, or other products where mastery matters more than engagement.
  • A point of view on how human relatedness and social connection shape learning
  • Previous experience in research labs, frontier tech companies, or startups with high autonomy and ambiguity.

What the JD emphasized

  • operate at the frontier where the right measures often do not exist yet and have to be built
  • build and validate the instruments, measures, and evaluation methods
  • derive insight from imperfect, dynamically changing data
  • making research decisions with incomplete information while holding a high bar
  • Comfort with ambiguity and undefined problem spaces

Other signals

  • design and run mixed-methods studies on how people develop real skill with AI
  • build and validate the instruments, measures, and evaluation methods
  • translate research insights into shipped product, curriculum, and model-level improvements
  • create tools using code and software to collect validated metrics at scale