Learning Engineer

About the Role Learnvia develops research-based, interactive, and free-to-students courseware, tools, and supports that empower educators and unlock student success in gateway math. Founded in 2025 as a nonprofit learning collaborative affiliated with Carnegie Mellon University, Learnvia is supported by Gates Foundation funding for courseware development, technology infrastructure, professional learning development and ongoing research through the inception and implementation phases. This investment enables Learnvia to innovate boldly while remaining grounded in evidence and impact. Grounded in learning science, Learnvia’s approach translates rigorous research into high quality, engaging courseware that elevates instructional excellence, strengthens iterative and evidence-based teaching practices, and advances student success in critical prerequisite coursework for higher level studies. The first pilot program launched in Fall 2025 with Calculus 1, with additional high-enrollment, high-attrition gateway courses to be added over time, based on research findings and continuous feedback from faculty and students. Learnvia draws on Carnegie Mellon University’s (CMU) expertise in mathematics education, learning sciences, and AI to reimagine a research-informed math curriculum designed to dramatically advance teaching practices to improve student learning and postsecondary outcomes. As a nonprofit working in close partnership with faculty from a range of institutions across the country as well as leading technology, curriculum, and research organizations, Learnvia is deeply committed to continuous improvement – designing, testing, and refining its courseware to remain innovative, evidence based, and responsive to the needs of faculty and students. Learnvia is governed by a five-member board with representation from both Carnegie Mellon University and the Gates Foundation. The organization’s growing team includes experienced educators, researchers, and educational technologists. We are seeking a Learning Engineer with a background in math instruction to help us deeply understand how learners interact with our platform and how their skills grow over time. You’ll make sure our platform is properly instrumented to capture meaningful learning data, then analyze that data to uncover insights that improve learner outcomes. This role sits at the intersection of learning science, data, and product — ideal for someone who wants to shape how modern learning platforms measure and accelerate skill development. Core responsibilities include: Partner with engineering to design and implement event tracking across lessons, assessments, and practice activities. Ensure logging schemas capture fine-grained learner interaction data such as attempts, hints, time-on-task, persistence, and error patterns. Audit and maintain data quality so insights are trustworthy and actionable. Analyze learner data to identify opportunities for improvement in mathematics instruction, online course design, and student support, coordinating efforts to implement changes and measure their impact on learning outcomes. Apply learning science models (e.g., learning curves, item response theory, knowledge tracing) to track skill growth, conceptual understanding, and predict mastery. Identify where learners struggle in mathematics content and what drives retention, progression, engagement, and persistence. Translate findings into recommendations for product, content, and instructional design improvements, particularly in math learning experiences. Collaborate and communicate closely with product managers, instructional designers, subject matter experts, and engineers to shape new features and interventions informed by learner data and evidence-based math pedagogy. Support A/B testing and experiments to measure the effectiveness of instructional interventions and learner supports. Communicate research findings clearly across a range of formats (e.g., written reports, dashboards, presentations) to diverse internal and external audiences, including business-oriented stakeholders. You should demonstrate: An ability to clearly communicate complex analyses as actionable insights for non-technical audiences. A passion for improving learner outcomes through data-driven and iterative instructional design. Strong understanding of mathematics teaching and learning, including common learner misconceptions and challenges in secondary or postsecondary math education. Interest in applying learning science and analytics to improve mathematics instruction at scale. Qualifications: Undergraduate degree and at least two (2) years of experience in Learning Sciences, Data Science, Mathematics Education, Educational Technology, or a related field required. Experience teaching mathematics for at least two (2) years at the high school or college level required. Knowledge of mathematics pedagogy, evidence-based instructional practices, and student learning in quantitative disciplines required. Knowledge of learning science research, best practices in pedagogy and andragogy, and research-based principles of multimedia design required. Experience analyzing learner or user interaction data in large-scale learning platforms (higher education learning platforms is a plus) required. Experience with cognitive modeling, learning curves, educational data mining, or learning analytics required. Experience working with event logging, data pipelines, or instrumentation frameworks strongly preferred. Strong statistical skills (SQL, Python, R) strongly preferred. Degree, coursework, or demonstrated expertise in mathematics education, mathematics, STEM education, or a related discipline strongly preferred.