This paper explores the application of Learning Engineering principles to enhance the skills of learning designers and faculty. Learning Engineering integrates data from instrumented sources—including learning analytics, formative assessments, and learner feedback—to inform the iterative design of educational content aimed at optimizing learner outcomes. Drawing on interdisciplinary methodologies from assessment, cognitive science, computer science, and data science, Learning Engineers (LEs) “unpack expertise” to create effective educational technologies, data models, and analytic systems. Central to this approach are Adaptive Instructional Systems (AIS), which not only deliver personalized learning experiences but also support continuous improvement through systematic data collection and analysis. The Learning Engineering Fellowship Program (LEFP) was developed to address the evolving needs of educational professionals in evidence-based design and learning outcome measurement. This case study details the creation, implementation, and iterative refinement of the LEFP, highlighting its impact on participant engagement and professional development. The findings underscore the potential of learning engineering practices to scale effective learning solutions and inform future innovations in educational design.