Researcher, Education Labs

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.
• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a field relevant to the role.

Nice-to-Haves

• 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.