Nicholas Groszewski
Senior Software Engineer at Google DeepMind
United States
Summary
🤩
Rockstar🎓
Top SchoolNicholas Groszewski is a Staff Software Engineer with a decade of experience building scalable ML infrastructure and production systems, currently focused on Gemini AI infrastructure at Google DeepMind. He progressed through engineering and senior roles at Google and Capital One, bringing a strong background in data engineering, MLOps, and backend/devops. Nicholas is an active open-source contributor to prominent projects like TensorBoard and Seldon Core, where he improved build systems, proto/gRPC integrations, and compatibility with modern dependencies—work that helps critical ML tooling stay production-ready. He holds an MS in Computer Science from Georgia Tech and a BE from Duke, and pairs rigorous academic training with hands-on systems design. Colleagues would note his knack for quietly modernizing legacy builds and cross-language (Python/Rust/gRPC) interoperability to keep complex pipelines maintainable.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering - BE Electrical and Computer Engineering Computer Science, Bachelor of Engineering - BE Electrical and Computer Engineering Computer Science at Duke UniversityMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Georgia Institute of Technology
English
Github Skills (21)
kubernetes10
python10
machine-learning10
go10
tensorflow10
build-automation10
kubernetes-pods10
protobuffer10
protobuf10
grpc10
github-ci9
deploying9
cicd9
bazel9
mlops9
Programming languages (11)
Github contributions (5)
tensorflow/tensorboard
Jun 2022 - Jan 2023
TensorFlow's Visualization Toolkit
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:1 release, 93 reviews, 26 commits in 7 months
Contributions summary:Nicholas's contributions primarily revolved around enhancing the TensorFlow TensorBoard visualization toolkit. They focused on updating the build process, specifically requiring Python 3.7+ and integrating flake8 for code quality checks. Additionally, they updated the project's build configurations, added new source metadata to support logging, and refactored existing code for efficiency. Furthermore, they made changes related to the Rust-based data server, ensuring the project's compatibility with current dependencies.
machine-learningtensorflowvisualization
SeldonIO/seldon-core
Apr 2020 - Nov 2020
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:MLOps Engineer
MLOps EngineerContributions:1 review, 7 commits, 9 PRs in 7 months
Contributions summary:Nicholas's commits primarily focus on updating and maintaining the project's infrastructure, with an emphasis on ensuring compatibility with newer versions of related packages and libraries. They addressed issues related to proto compilation, package versions, and gRPC integration within the executor service. The user also worked on expanding the project's functionality by adding support for custom data handling within the prediction protobuf, alongside adding example notebooks and documentation for new features. Furthermore, they made changes to accommodate Istio support by altering service port names.
monitorservingdata-sciencedeploymentmachine-learning
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