Senior Researcher, Applied Machine Learning

foundry10 is an education research organization with a philanthropic focus on expanding ideas about learning and creating direct value for youth. In collaboration with a wide range of partners, we surface, evaluate, and share opportunities to better support youth learning both inside and outside the classroom. Building on more than a decade of impactful work, our unique approach blends applied and basic research, philanthropy, and education programs rooted in evidence-based best practices.

Summary of Role

Machine Learning can provide powerful tools for enhancing learning in and outside of the classroom, and for addressing pressing needs in K-12 and post-secondary education. The Senior Researcher, Applied Machine Learning will be a core team member on a new multi-project focused ML Learning Team at foundry10. This role involves both independent research and analysis as well as multidisciplinary collaboration across a project team and potentially in partnership with external institutions.

Applicants for this role should have a strong interest in education and learning with a desire to improve student outcomes across multiple dimensions. Additionally, applicants should have deep expertise in appropriate domains (data processing, computational approaches, model development, evaluation, and deployment), bring novel ideas and expertise, and experience-based insights. Qualified candidates will also bring the ability to communicate findings through multiple formats, including peer-reviewed publications, technical reports, public-facing writing, prototypes, tools, or other research products.

This position reports within the Research Pillar. The salary range for this position is $191,025 to $318,375 per year, depending on experience.

Areas of Interest

The Senior Researcher will help develop a collaborative research agenda focused on how machine learning can expand what we are able to understand, measure, design, and support in learning environments. This work may include formal and informal K–12, post-secondary, and out-of-school learning contexts.

We are interested in candidates who find the following topics compelling:

• Exploring how ML and AI can help us better understand human learning processes, developmental trajectories, reasoning and other complex dimensions of learning
• Considering principles and knowledge that are not being regularly utilized by traditional educational systems to broaden and shape new ideas around the intersection of learning and ML
• Designing or repurposing tools for research pursuits, data collection, learning gains, and timely interventions
• Identifying how emerging ML methods can help generate new forms of assessment, feedback, reflection, accessibility, or learning support beyond conventional chatbot or tutoring applications

Responsibilities

As part of a focused project team, this role in particular will:

• Work collaboratively within the ML project team to provide new and interesting ideas for lines of research, outputs, tool development and/or other avenues to improve learning outcomes and opportunities for young people
• Collaborate to define output goals for the project team
• Conceive, design, build, adapt, and/or evaluate ML models, research systems, datasets, prototypes, analytic pipelines, and, where appropriate, software or hardware-enabled tools.
• Think critically about data quality, experimental design, and responsible AI practices
• Develop predictive analytics, statistical modeling, data mining and machine learning algorithms
• Crafting interesting and important questions regarding education in a technologically advancing world, specifically around the intersections of ML/AI in learning spaces and attempting to answer these questions through research.

More broadly as a researcher, this role will include:

• Represent foundry10 at conferences, within publications, and face-to-face with a variety of community partners
• Actively read and engage with current research/techniques/technologies and bring new knowledge to the organization and their teams as a result of engagement with relevant topics
• Use various qualitative and quantitative methodologies, analysis strategies, and study designs as needed to address current research questions
• Make informed decisions about when to extend vs. redirect or close off lines of inquiry, depending on progress and likelihood of yielding useful and actionable insights
• Lead operations and partnership tasks (e.g., coordination of external data collection services, communication with external partners) as needed

This is not an exhaustive list, other duties may be assigned as necessary