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Michael Brewer has a diverse and extensive work experience in the software development industry. Michael started their career at Aspen Technology (ICARUS) as a Software Developer, where they created front-end display and CAD tools for a chemical engineering process modeling and costing application. Michael then joined Freedom Scientific as a Software Engineer and served as the development team lead for the MAGic screen magnification product. During their time there, they worked with technologies such as C++, Win32, MFC, video driver development, and Windows event hooking.

In 2003, Michael joined the University of Washington as a student, pursuing studies towards a certificate in Advanced Web Programming. Michael gained experience in C#, SQL Server, and ASP.NET. After completing their studies, they moved on to Microsoft as a Software Development Engineer III, working on the SQL Server Setup team. Michael contributed to sustaining engineering and new development projects, utilizing C++, Win32, MSI, and SQL technologies.

Currently, Michael is a Senior Software Development Engineer at RAF Technology. Michael'sprimary focus is on RAF's flagship product for automated mail sorting. Michael is responsible for writing high-performance code for real-time image recognition using C/C++, C#, algorithms, and machine learning technologies.

Throughout their career, Michael has consistently demonstrated their expertise in software development, utilizing various programming languages and technologies in different domains, including mail sorting, screen magnification, chemical engineering, and SQL servers.

Michael Brewer has a Master of Science degree in Computer Science from The Johns Hopkins University. Michael also holds a Bachelor of Science degree in Physics from the University of Maryland. In addition to their formal education, they have obtained several certifications including "Algorithms: Design and Analysis, Part 2" from Coursera (via Stanford University) in October 2013, "Algorithms: Design and Analysis, Part 1" from Coursera (via Stanford University) in March 2013, and "Neural Networks for Machine Learning" from Coursera (via University of Toronto) in November 2012.