Top expert inScala Ecosystem and Reactive Programming
Paul Phillips is an advisor and leader in commercial AI and ML automation with nearly two decades of experience building and scaling high-performing teams and products that turn machine learning into measurable business outcomes. A two-time founder who successfully exited VC-backed startups, he has led enterprise transformations—from real-time programmatic advertising systems making millions of bid appraisals per second to Deloitte Digital’s CortexAI platform with a 200+ person team. Equally comfortable in the codebase and the C-suite, Paul has hands-on open-source contributions to core Scala projects (including scala, scalacheck and sbt) that reflect deep engineering rigor behind his strategic work. Known for raising funds, accelerating revenue without proportional headcount growth, and fostering collaborative cultures, he blends entrepreneurial grit with enterprise-scale delivery. Based in Reno, he brings a rare mix of practical systems-level coding experience and proven commercial leadership in AI-driven automation.
A more featureful runner for sbt, the simple/scala/standard build tool
Role in this project:
DevOps Engineer & Automation Engineer
Contributions:203 commits, 51 PRs, 43 pushes in 6 years 9 months
Contributions summary:Paul primarily focused on maintaining and updating the `sbt-extras` build tool launcher and associated test scripts. They updated launcher versions (0.12.0, 0.12.1, 0.12.2, 0.12.3, 0.12.4, 0.13.0, 0.13.2, 0.13.5, 0.13.6, 0.13.7, 0.13.8) as well as refactoring and deduplicating test code, improving test suite maintainability. They also implemented updates related to test execution and build properties.
Scala 2 compiler and standard library. Scala 2 bugs at https://github.com/scala/bug; Scala 3 at https://github.com/scala/scala3
Role in this project:
Back-end Developer
Contributions:1 review, 1720 commits, 6 PRs in 5 years 8 months
Contributions summary:Paul contributed to the Scala language compiler and standard library, primarily focusing on enhancements to the interpreter and type system. Their work involved modifying code related to macro expansions, type parameter inference, and error handling, as well as addressing bugs in value class and generic type handling. Furthermore, they implemented improvements related to classfile parsing, ensuring correct handling of various Java features and a general improvement to bytecode tests.
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Paul Phillips - Advisor Leader, Commercial AI & ML Automation