This introduction to the book provides a conceptual and historical overview of learning engineering. Although its formal definition is still evolving, learning engineering aims to optimize specific learning solutions--from the learning sciences to human-centered design methodologies to data-informed decision-making--in order to understand under what conditions and with what learners a current design is optimal or not, and to develop and test alternative more robust, or more refined, solutions that are more scalable. The author makes the case for learning engineering as a multidisciplinary approach that complements related professional practices and fields of study such as instructional design, learning sciences, data analytics, instructional systems design, and more. After a brief exploration of the differences between science from engineering, this introduction goes on to address the theoretical and professional origins of learning engineering as well as its inherently team-based process, using as examples the language-learning platform Duolingo and the Carnegie Mellon University spinoff Carnegie Learning, Inc. to discuss effective techniques.