Steven Merrill is a Principal Software Engineer with over 16 years of experience building high-performance web platforms, scalable LAMP architectures, and interactive multimedia experiences. He blends hands-on DevOps and backend work—tuning Varnish, SOLR, memcached and MongoDB—with front-end performance optimization and CI automation using Jenkins. His career spans leadership and engineering roles at companies like Phase2, Formlabs, Liteboxer, and currently ExamSpark, where he focuses on reliable, scalable delivery. An active open-source contributor, he has improved HashiCorp Packer builders for VMware and VirtualBox, adding image compaction and headless build features that streamline multi-platform image creation. A former university instructor in interactive and mobile multimedia, he brings both practical production expertise and an educator’s clarity to complex problems. Outside of engineering he’s a musician, cyclist, and fermentation enthusiast, which underscores a creative, experimental approach to systems design.
16 years of coding experience
18 years of employment as a software developer
B.A. Music Multimedia and Computer Science, B.A. Music Multimedia and Computer Science at Bradley University
Packer is a tool for creating identical machine images for multiple platforms from a single source configuration.
Role in this project:
Backend & DevOps Engineer
Contributions:37 commits, 1 PR, 2 comments in 2 years
Contributions summary:Steven contributed to the Packer project by enhancing the VMware builder. They implemented features like uploading VMware Tools, adding a compaction step for disk images, and enabling headless mode for VMware and VirtualBox builds. Furthermore, the user incorporated code review feedback, improved the build process, and fixed existing issues within the Amazon builder.
An rsync watcher for Vagrant 1.5.1+ that uses fewer host resources.
Contributions:1 release, 72 commits, 8 PRs in 7 years 3 months
vagranthostrsyncwatchervagrantfile
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Steven Merrill - Principal Software Engineer at ExamSpark