Radu Munteanu - Engineering at Apple

Radu Munteanu

Engineering at Apple

Los Altos, California, United States

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Summary

👤

Senior

🎓

Top School

Radu Munteanu is an engineering leader with 11+ years building high-quality, scalable systems and driving DevOps/SRE practices at companies from Intuit and Netflix to Apple. He blends deep hands-on expertise in Scala, PlayFramework, Java, and automated testing with practical MLOps experience—contributing core backend work and Python test integration to the widely used H2O-3 open-source ML platform. Known for cultivating engineering quality, he has led QE and BDD/TDD initiatives, performance engineering, and CI/CD automation across cloud and virtualization stacks (AWS, OpenStack, ESXi). Radu’s background spans low-level C++ systems and math-heavy algorithm work to orchestration with Kubernetes, Docker, Chef and Puppet, enabling full lifecycle reliability. Based in Los Altos, he pairs mentoring and platform thinking with a knack for adding pragmatic reporting and test infrastructure that surfaces real-world model and system behavior. An early practitioner of behavior-driven development and continuous deployment, he favors elegant, measurable solutions that scale.

11 years of coding experience

21 years of employment as a software developer

Liceul Nicolae Balcescu

MS, Mathematics and Computer Sciences, MS, Mathematics and Computer Sciences at Babes-Bolyai University

Github Skills (19)

back-end-development10

machine-learning10

java10

javas10

h2o10

api-design9

python9

api9

automl9

build-tools8

deep-learning8

test-framework8

release-management8

build-system8

testing8

Programming languages (1)

Jupyter Notebook

Github contributions (4)

h2oai/h2o-3

Oct 2014 - Dec 2014

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Role in this project:

Back-end Developer & MLOps Engineer

Contributions:37 commits in 2 months

Contributions summary:Radu contributed to core functionalities in the H2O-3 repository, focusing on Java code for the machine learning platform. Their work included modifications to core data structures (Freezable, Value), MapReduce tasks, and API routing, indicating a deep understanding of the platform's internals. Furthermore, the commits show integration of python tests to validate and manage H2O models, showcasing the user's involvement in MLOps and the end-to-end machine learning lifecycle within H2O. The user was also involved with adding HTML reporting to summarize the tests, adding value to the model testing process.

xgboostgampythonk-meansautoencoders

radugm/skeleton

Mar 2016 - Mar 2016

Contributions:1 push in 1 day

node-expressexpress-mongodbexpressnode-express-mongodbnodejs

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