Eric Hulburd
Lead MLOps Engineer at Rigetti Computing
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolEric Hulburd is a Lead MLOps Engineer in Berkeley with 12 years of experience applying classical ML to improve quantum hardware performance and building the data systems that enable that research. He blends full-stack engineering, cloud authorization/authentication (gRPC, Casbin, OpenStack, AWS), and data platform work to deliver production-ready observability, BI, and research tooling. At Rigetti he moved from implementing secure cloud services to leading MLOps for quantum ML, drawing on a background that includes Rails-backed carbon-footprint analytics and GreenButton data parsing. An active open-source contributor, he has improved front-end benchmarking by integrating Dioxus into the popular js-framework-benchmark and contributed backend work to Rigetti’s pyQuil quantum SDK. With advanced training in data science from UC Berkeley and a humanities-informed Master’s from Stanford, he pairs technical depth with cross-disciplinary thinking and a demonstrated knack for shipping robust systems under ambiguous research constraints.
12 years of coding experience
8 years of employment as a software developer
Master's degree, Data Science, Master's degree, Data Science at UC Berkeley School of InformationBachelor of Science, Environmental Engineering, Bachelor of Science, Environmental Engineering at Stanford University
English, Spanish, Portuguese
Github Skills (15)
component-development10
quantum-computing10
web-framework10
javascript10
frontend10
quil10
python10
web-frontend10
pytest9
benchmarking9
webassembly9
benchmark9
api8
rust8
apidoc8
Programming languages (17)
Github contributions (5)
rigetti/pyquil
Dec 2019 - May 2022
A Python library for quantum programming using Quil.
Role in this project:
Backend Developer
Backend DeveloperContributions:46 reviews, 25 commits, 34 PRs in 2 years 5 months
Contributions summary:Eric primarily contributed to the backend of the pyQuil library, focusing on the integration and management of the QCS ISA, along with improvements to configuration and error handling. Their work included refactoring ISA management, integrating the new QCS ISA, and ensuring accurate gate information. Additionally, the user made updates to testing infrastructure, as well as improvements to the API to address noisy QPUs.
pythonforestquilquantum-computingquantum-programming
krausest/js-framework-benchmark
Jun 2022 - Jun 2022
A comparison of the performance of a few popular javascript frameworks
Role in this project:Front-end Developer
Front-end DeveloperContributions:5 commits in 25 days
Contributions summary:Eric's primary contribution involves integrating the Dioxus framework into the js-framework-benchmark repository, specifically focusing on the keyed implementation. They added a new Dioxus framework and included initial code. The user further updated the Dioxus implementation with build optimizations and fixed keying issues within the row component. These changes suggest a focus on component implementation and performance improvements within the context of a front-end benchmarking project.
javascript-framework
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