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

About the company

Learning Commons provides a personalized learning platform for K-12 education, offering standards-based supplemental curriculum, intervention resources, and teacher tools that enable customized, student-centered learning across in-class, hybrid, and remote settings. Its platform acts as a learning management system that helps educators create individualized learning paths, track progress, and deploy competency-based activities at the student’s own pace. Unlike some EdTech tools that offer generic content, Learning Commons emphasizes teacher-enabled personalization and instructional coaching, rooted in a coaching founder’s approach to research-based, student-centered learning. The company’s goal is to move beyond one-size-fits-all instruction by equipping teachers with resources and workflows to support deeper engagement and differentiated outcomes for every student.

Company Size

1-10

Company Stage

N/A

Total Funding

N/A

Headquarters

N/A

Founded

N/A

Get referred to Learning Commons

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Simplify's Take

What believers are saying

September 2026 launch added Canva Education, MagicSchool, and TalkingPoints integrations.
August 6, 2026 ChatGPT Edu and ChatGPT for Teachers now connect to Knowledge Graph.
July 14, 2026 Claude for Teachers integration gives Learning Commons immediate distribution.

What critics are saying

OpenAI and Anthropic integrations turn Learning Commons into a replaceable upstream layer.
FERPA, COPPA, and arbitration-heavy terms raise procurement friction with school districts.
If builders stop using its datasets, the nonprofit loses relevance and funding traction.

What makes Learning Commons unique

Open K-12 AI infrastructure, not a district LMS, differentiates Learning Commons.
September 22, 2026 platform bundles Knowledge Graph, Evaluators, and Agent Skills.
Free machine-readable standards, progressions, and rubrics lower integration costs for builders.

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Benefits

401(k) Retirement Plan
401(k) Company Match
Paid Time Off
Family Planning Benefits
Relocation Assistance

Company News

TutorFlow

Sep 23rd, 2026

Learning Commons opens K-12 AI standards platform.

Learning Commons opens K-12 AI standards platform. What happened. EdSurge reports that Learning Commons, a Chan Zuckerberg Initiative-backed nonprofit, launched an open platform for educators and edtech developers on September 22, 2026, with integration partnerships involving Canva Education, Level, MagicSchool, OKO Labs, Really Great Reading and TalkingPoints. Its Knowledge Graph provides open-license K-12 data, while Evaluators assess AI-generated content for accuracy, grade fit, evidence and standards alignment. Why it matters. For K-12 teams and vendors, the practical issue is less novelty than workflow. The source says the datasets include academic standards from all 50 states, learning progressions, curricula and related materials, which could reduce repeated standards-mapping work. Tambra Clark of Birmingham City Schools said clean, machine-readable formats could help districts and small developers avoid "reinventing the wheel." Helen Crompton of Old Dominion University was "cautiously optimistic," but warned that infrastructure can shape what is considered authoritative and what gets measured. What to do next. Treat the platform as shared infrastructure to inspect, not a plug-and-play substitute for local review. Check whether any generated materials match your state standards, age level, curriculum context and assessment purpose. Note Lisle District 202's beta use: Jason Markey said the team needed outside technical help to make an internal tool launchable. Review hosting, data analysis and student privacy obligations, and compare Learning Commons outputs with other evidence sources before relying on them. About this briefing Discussion (0). Open to everyone No comments yet. Start the conversation with a practical takeaway. Build with what you learned Turn a useful idea into teaching content. Use TutorFlow to build courses, videos, slides, tests, and interactive activities from your own materials.

Learning Commons

Apr 13th, 2026

Learning Commons announces early access to new Literacy Evaluators.

Learning Commons announces early access to new Literacy Evaluators. Explore new tools designed to close the gap between surface-level readability and real comprehension. Six months ago, Learning Commons launched Learning Commons to scale proven teaching and learning practices to benefit every learner. Learning Commons is focused on developing open infrastructure to improve the quality of AI-powered tools in education, from datasets grounded in learning science to systems that help evaluate how well tools actually support teaching and learning. Since then, Learning Commons has further developed its tools in close partnership with researchers, educators, and developers. At the ASU+GSV Summit, Learning Commons announced early access to two new Literacy Evaluators: Subject Matter Knowledge and Conventionality. * The Subject Matter Knowledge Evaluator identifies the hidden background knowledge a reading passage assumes, so developers can see whether text is truly accessible for a given grade, not just "on-level" by readability. This evaluator enables better prompting by giving developers the information they need to optimize prompts, generate scaffolds, and maintain quality at scale. * The Conventionality Evaluator analyzes how directly a text communicates its meaning. It identifies whether language is literal and straightforward or layered with figurative expressions, idioms, irony, and implied meaning. Standard readability tools tell you a text's grade level, but not whether its meaning requires real interpretive work to unpack. This evaluator fills that gap by rating a text's conventionality complexity, identifying the specific language features driving it, and offering actionable suggestions for scaffolding or instruction so developers can make intentional choices about the texts generated rather than discovering a problem after the fact. Building better tools starts with better foundations. Its new Subject Matter Knowledge and Conventionality Evaluators are designed to close the gap between surface-level readability and real comprehension. When developers can see not just what a text says, but what it demands of a reader, they can build with far greater intentionality and impact for educators and learners. Sandra Liu Huang, president of Learning Commons What's next. Its work is guided by a belief that shared foundations make it easier to build, evaluate, and improve tools in ways that are transparent, evidence-based, and grounded in how students learn. Learning Commons is pleased that this vision is resonating with the field. Since September, its tools and resources have been downloaded more than 8,000 times. This summer, Learning Commons will launch a platform that is designed to lower barriers for those who want to build and evaluate AI-powered education tools responsibly. Starting today, developers can get early access to the platform, which brings together API endpoints, MCP tools, and SDKs. Developers can now discover, explore, and start building with its tools within their existing workflows. Learning Commons is excited to offer a Knowledge Graph Explorer and an Evaluator Playground. * The Knowledge Graph Explorer allows builders to view the structure and content of its datasets in Knowledge Graph; no prior technical knowledge required. The Knowledge Graph Explorer makes it easy to explore datasets visually, so you can quickly build a mental model of how learning concepts, standards, and relationships are structured before writing code or integrating. * The Evaluator Playground lets developers experiment in real time, test ideas, and see what works. You can run an evaluator against a piece of text and immediately see how the output changes. The Playground is an interactive, browser-based environment for evaluating text passages for student instruction in real time. No local setup is required. Learning Commons is excited to provide early access to the platform and invite developers, researchers, and partners to build with Learning Commons alongside a broader ecosystem investing in shared infrastructure for the long term. This summer, Learning Commons will also further expand access, introducing additional features, integrations, and licensing options to support adoption at scale. Learning Commons is also welcoming Laurence Holt to the Learning Commons Advisory Board. Holt joins experts from a range of fields who are helping guide its efforts to develop open, shared AI resources for K-12 education. He is a senior advisor at XQ Institute and Teaching Lab, and a partner in the Coherence Fund. Holt was previously chief product officer of Amplify, where he oversaw the design and development of products used by millions of K-12 students and teachers. A shared investment in open infrastructure. In parallel with its product development, Learning Commons is supporting the K-12 AI Infrastructure Program led by Digital Promise. Over four years, the program will fund the development of openly shared datasets, models, benchmarks, and other digital public goods that anyone can freely use, adapt, and build upon to help close the gap between learning science and the capabilities of generative AI. Rather than funding isolated tools, the program is designed to strengthen the field's shared foundations, creating greater consistency, rigor, and accountability across it.