The $190 billion Elementary and Secondary School Emergency Relief (ESSER) fund enabled U.S. schools to implement evidence-based strategies to address pandemic learning loss. With the expiration of these funds, integrating such interventions sustainably into school structures has become a pressing challenge. High-dosage tutoring (HDT) emerged as one of the most widely adopted interventions due to its proven effectiveness; however, its significant cost makes it particularly vulnerable to budget cuts as ESSER funds expire. This study examines how learning engineering principles can support the system-wide adoption of HDT, focusing on aligning interventions with institutional constraints, fostering productive school cultures, and leveraging real-time data tracking for continuous improvement. Synthesizing findings from 31 organizations serving over 300,000 students, this research identifies key strategies to overcome logistical barriers, such as scheduling, tutor recruitment, and curriculum alignment, while maintaining the efficacy and scalability of HDT. This analysis highlights the critical role of school partnerships and human-centered design in embedding personalized learning practices into daily routines, ensuring sufficient dosage, and expanding the tutor pipeline. By employing iterative learning engineering processes, this study demonstrates how data-informed decisions can drive systemic changes at school, district, and state levels, creating sustainable and equitable tutoring models. The findings also highlight the gap between advancements in learning science and their large-scale implementation, emphasizing the need to individualize learning experiences and calibrate difficulty levels to enhance student engagement and achievement. This research provides actionable insights for policymakers, educators, and learning engineers aiming to integrate personalized learning as a core educational practice, fostering long-term academic growth and equity.