Adam Goode is a seasoned software engineer with 22 years of experience building and modernizing complex systems, from migrating large Python 2 codebases to Python 3 to leading multi-quarter, revenue-critical service migrations at Google. He combines deep systems and SRE expertise—responsible for low-latency ads serving and resource-efficiency optimization—with hands-on engineering across languages and platforms, including Linux backend work for Chromium Web MIDI. An active open-source contributor, he has improved portability in the MLton compiler (adding ARM and RISC-V support) and helped evolve widely used projects like Home Assistant and OpenSlide. After three years as an engineering manager he prefers individual-contributor roles where he mentors teammates and improves engineering culture. Based in Pittsburgh, he brings a pragmatic focus on maintainability and operational reliability informed by extensive on-call and platform-migration experience.
22 years of coding experience
24 years of employment as a software developer
BS, Computer Science / Psychology, BS, Computer Science / Psychology at Rensselaer Polytechnic Institute
MHCI, Human-Computer Interaction, MHCI, Human-Computer Interaction at Carnegie Mellon University
Contributions:21 commits, 41 PRs, 18 pushes in 2 years 4 months
Contributions summary:Adam primarily contributed to the backend functionality of the Google Cloud Print Connector. Their work involved fixing bugs related to logging, enabling logging to the systemd journal, and updating log message severities. They also refactored code, renamed project identifiers and import paths, and made changes to the Privet API, indicating responsibility for core connector logic and system integration. Furthermore, the user implemented exponential backoff and handled mDNS collisions.
Contributions:2 reviews, 899 commits, 3 PRs in 9 years 5 months
Contributions summary:Adam's contributions primarily involve working with a library focused on reading and processing whole-slide image files, specifically focusing on integrating and enhancing support for the Hamamatsu VMU file format. They implemented code to parse and extract data from the VMS files. The user's changes span various aspects of the library, including integrating new functionalities related to JPEG compression, data parsing, and the incorporation of associated images.
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