Steven Deitz is a seasoned software engineer with eight years of experience currently based in Seattle and working at Google, bringing a deep background in high-performance computing from earlier roles at Cray and Microsoft. He holds a Ph.D. in Computer Science from the University of Washington and a BA in Mathematics and Computer Science from Bowdoin, combining rigorous academic training with production-grade software development. His open-source contributions include backend work on GoogleCloudPlatform/PerfKitBenchmarker, where he expanded cloud benchmarking capabilities—adding HPCC metrics parsing and Azure integrations—to improve accuracy of cloud performance tests. Comfortable across large-scale cloud ecosystems and HPC tooling, he specializes in building reliable benchmarks and integrations that surface real-world performance differences. Colleagues know him for methodical, test-driven changes and for bridging research-grade algorithms with pragmatic engineering at scale.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Washington
Bachelor of Arts (B.A.), Mathematics and Computer Science, Bachelor of Arts (B.A.), Mathematics and Computer Science at Bowdoin College
PerfKit Benchmarker (PKB) contains a set of benchmarks to measure and compare cloud offerings. The benchmarks use default settings to reflect what most users will see. PerfKit Benchmarker is licensed under the Apache 2 license terms. Please make sure to read, understand and agree to the terms of the LICENSE and CONTRIBUTING files before proceeding.
Role in this project:
Back-end Developer
Contributions:1 release, 2 reviews, 157 commits in 3 years 10 months
Contributions summary:Steven's commits focused on expanding the PerfKit Benchmarker project's capabilities by implementing features related to HPCC metrics parsing, Azure cloud service integrations, and enhancing the object storage service benchmark. They developed new functionalities by adding methods to parse metrics, integrating new Azure-related libraries, and modifying existing code to improve the accuracy of the test. They also contributed to adding and modifying tests for existing features to ensure that all changes were successful.
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