Researcher, Education Labs

About the role

We believe that learning is fundamental to human agency. Education Labs studies how people learn and build capability with AI, and we close the loop between what we discover and learning tools the world can access.

This is a hands-on research role embedded in a small team. You will publish and influence thinking across Anthropic, and you will also help decide what is working well enough to scale, what should be handed off to another team, and what should be spun down. We care about learning experiences that make people progressively more capable, curious, and empowered over time.

Responsibilities

• Design and run mixed-methods studies on how people develop real skill with AI, measuring success by capability growth rather than engagement.
• 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.
• Translate research insights into shipped product, curriculum, and model-level improvements through close collaboration with engineers, designers, and researchers.
• Generate net-new insights about how AI is reshaping learning, and how communities and organizations can organize to learn alongside it.
• Communicate your work through clear writing, prototypes, and presentations that shape thinking across the organization.
• Create tools using code and software to collect validated metrics at scale.

Requirements

• A research background in learning sciences, education, cognitive science, HCI, educational psychology, or a closely related field, whether formal or self-directed.
• Strong mixed-methods skills: experimental design, measurement and psychometrics, qualitative methods, and the judgment to choose the right approach for the question.
• Hands-on technical skill in Python, data analysis, and working with LLMs, enough to run your own analyses and prototype new measures.
• Comfort deriving insight from imperfect, dynamically changing data, and comfort making research decisions with incomplete information while holding a high bar.
• Comfort with ambiguity and undefined problem spaces, plus a bias toward rapid, iterative inquiry and quick learning loops.
• Clear communication and a track record of cross-functional collaboration with product, design, engineering, and research partners.
• Genuine curiosity about how AI is changing how people learn, work, and build capability, and a strong perspective on technology enhancing human capability rather than diminishing it.

Qualifications

• 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, and why they matter as people learn alongside AI.
• Previous experience in research labs, frontier tech companies, or startups with high autonomy and ambiguity.