Job description

Learning Commons aims to scale proven teaching and learning practices to benefit every learner by building AI infrastructure that better connects the way students learn to the tools they learn with. The Team At Learning Commons, we operate at the intersection of technology, research, and philanthropy. We pair product development with grantmaking to scale proven teaching and learning practices for the benefit of every learner. We aim to bring learning science into the tools educators and students use every day. Our work is grounded in a deep belief: when technology reflects the realities of classrooms and the science of how students learn, it can meaningfully strengthen teaching and unlock new possibilities for students. The rise of generative AI offers us a once-in-a-generation opportunity to dramatically accelerate the translation of research insights into practical, classroom-ready tools; tools that honor teachers’ expertise, adapt to students’ needs, and make effective learning practices easier to access, implement, and sustain. In today’s fragmented edtech landscape, school districts are often left piecing together products that don’t always align with curricula or instructional needs. While AI holds enormous potential to support teachers and students, it can only deliver on that promise when grounded in research, high-quality educational data, and expert evaluation. That’s why we’re building open, public-purpose infrastructure — datasets, rubrics, and resources — that help raise the standard for educational tools and create more consistent, impactful learning experiences for all students and teachers. The Opportunity Learning Commons aims to scale proven learning science practices through AI-powered tools, datasets, and evaluation frameworks. As part of the Evaluators team, you will play a critical role in ensuring that AI and education products are grounded in rigorous, research-backed evaluation. You will define and operationalize evaluation frameworks for AI-enabled learning tools, develop metrics and methodologies to assess quality and impact, and generate insights that inform product, research, and ecosystem decisions. This includes evaluating model performance, alignment to pedagogy, and real-world effectiveness in classrooms. You will partner closely with Product, Engineering, Learning Science, and external researchers to ensure that evaluation is embedded throughout the product lifecycle—from early experimentation to scaled deployment. This role sits at the intersection of data science, learning science, and AI system evaluation. What You'll Do Define evaluation frameworks for AI-powered education tools (e.g., LLM-based systems, adaptive learning systems) Design and analyze experiments across structured and unstructured data (A/B testing, quasi-experimental methods, causal inference) Translate findings into clear recommendations for product and research partners Collaborate cross-functionally with Product, Engineering, Learning Science, and external partners Contribute to best practices for responsible AI evaluation, including bias, fairness, and reliability What You'll Bring 5+ years of experience in data science, applied research, or quantitative analysis Proficiency in the modern data science tech stack, including Python, SQL, ML Familiarity with evaluation of generative AI systems (e.g., rubric-based evaluation, human-in-the-loop evaluation) Ability to communicate complex findings to technical and non-technical audiences Experience collaborating with cross-functional teams (product managers, engineers, researchers) in a fast-paced development environment Compensation The Redwood City, CA base pay range for a new hire in this role is $190,000 - $261,800. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. This position may be eligible to participate in our discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with our total rewards philosophy and may vary by role. Better Together As we grow, we’re excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process. Benefits for the Whole You We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. Provides a generous employer match on employee 401(k) contributions to support planning for the future. Paid time off to volunteer at an organization of your choice. Funding for select family-forming benefits. Relocation support for employees who need assistance moving If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role. #LI-Hybrid

Requirements

Must-haves

Learning Commons aims to scale proven teaching and learning practices to benefit every learner by building AI infrastructure that better connects the way students learn to the tools they learn with. The Team
Define evaluation frameworks for AI-powered education tools (e.g., LLM-based systems, adaptive learning systems)
Design and analyze experiments across structured and unstructured data (A/B testing, quasi-experimental methods, causal inference)
Translate findings into clear recommendations for product and research partners
Collaborate cross-functionally with Product, Engineering, Learning Science, and external partners
Contribute to best practices for responsible AI evaluation, including bias, fairness, and reliability
5+ years of experience in data science, applied research, or quantitative analysis
Proficiency in the modern data science tech stack, including Python, SQL, ML
Familiarity with evaluation of generative AI systems (e.g., rubric-based evaluation, human-in-the-loop evaluation)
Ability to communicate complex findings to technical and non-technical audiences
Experience collaborating with cross-functional teams (product managers, engineers, researchers) in a fast-paced development environment

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonsqlml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

401(K)

Paid Time Off