Michael Reneer
Senior Software Engineer at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Reneer is a Senior Software Engineer in Seattle with 13 years of experience building cross-platform frameworks and distributed ML systems, currently leading the communication frameworks that connect IoT devices and mobile apps at Google. He blends deep systems and mobile expertise—Objective-C, Python, C/C++—with a strong emphasis on code quality, testing infrastructure, and developer tooling, having led TensorFlow Federated development and mentored organization-wide Python best practices. His open-source contributions span high-profile projects in federated learning and privacy (TensorFlow Federated, TensorFlow Privacy) where he improved test coverage, refactored core components, and raised code consistency across large codebases. Previously at Microsoft and Numera he shipped large-scale mobile and cross-platform subsystems, and he pairs technical leadership with a curious, puzzle-driven mindset rooted in a background that combines English and Physics with computer technology.
13 years of coding experience
10 years of employment as a software developer
English & Physics, English & Physics at Florida Southern CollegeBachelor of Science (B.S.) in Liberal Studies, Computer Technology, Bachelor of Science (B.S.) in Liberal Studies, Computer Technology at University of Central Florida
Stackoverflow
2,431reputation
60kreached
27answers
5questions
macos
top-5%
Github Skills (30)
markdown10
python10
testing10
machine-learning10
software-quality10
refactor10
pelican10
privacy10
markdown-it10
tensorflow10
federated-learning10
static-site-generation10
refactoring10
documentation10
pytest9
Programming languages (5)
Github contributions (5)
google-parfait/tensorflow-federated
Sep 2018 - Jan 2023
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:17 releases, 21 reviews, 1179 commits in 4 years 5 months
Contributions summary:Michael's commits primarily focused on removing Python lint directives from several files related to optimization, including files for various machine learning models. This suggests the user worked on cleaning up or refactoring the code within the project, with a specific focus on the codebase's structure, and consistency with TFF's guidelines. The changes span files across multiple subdirectories which involve the core components of the system. The impact of these changes is improving overall code quality.
machine-learning
tensorflow/privacy
Aug 2021 - Sep 2022
Library for training machine learning models with privacy for training data
Role in this project:ML Engineer
ML EngineerContributions:1 release, 59 commits, 11 PRs in 1 year 1 month
Contributions summary:Michael's contributions primarily involve modifying and testing code related to differentially private machine learning models within the TensorFlow Privacy library. Their commits demonstrate a focus on testing the functionality of tree aggregation queries and other components, including modifying existing test cases and fixing lint errors. The user also removed unnecessary dependencies and updated the project's setup.py to reflect the most current versions of the packages.
machine-learning-modelstraining-datamachine-learningprivacy
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