Marianne Monteiro
Research Engineer at Google DeepMind
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMarianne Monteiro is a research engineer with 11 years of experience at the intersection of machine learning, education and music, currently working at DeepMind in London. She has a strong track record in both research and developer-facing work—creating TensorFlow tutorials and developer workshops that reached hundreds, and improving usability and clarity for privacy-preserving ML tools like PySyft. Her background spans internships and research roles in anomaly detection, distributed systems and cloud computing, and substantial open-source documentation contributions to high-profile projects such as tensorflow/docs. Marianne combines rigorous research instincts with a knack for translating complex ML ideas into clear educational materials, making her equally effective with experiments and with enabling broader adoption. An often overlooked strength is her early work modernizing data-source abstractions for OpenStack Sahara, demonstrating deep systems thinking beyond typical ML research tasks.
11 years of coding experience
7 years of employment as a software developer
Ciência da Computação, Ciência da Computação at Universidade Federal de Campina GrandeColégio GEO João Pessoa
Portuguese, English
Github Skills (8)
tensorflow10
documentation10
technical-writing10
machine-learning9
text-generation8
python7
pytorch3
federated-learning3
Programming languages (7)
Github contributions (5)
OpenMined/PySyft
Nov 2018 - Sep 2019
Perform data science on data that remains in someone else's server
Role in this project:
Technical Writer
Technical WriterContributions:323 commits, 126 PRs, 218 pushes in 9 months
Contributions summary:Marianne's commits primarily focus on refining and improving the introductory tutorial for PySyft. They corrected typos, clarified the scope note, and made general stylistic improvements across multiple tutorial sections. Their work included correcting technical details in code, and enhancing the overall presentation and clarity of the tutorial content. This demonstrates a focus on documentation and user experience in the context of the PySyft project.
pytorchcryptographyacquiringpythonscience
tensorflow/docs
Sep 2018 - Oct 2018
TensorFlow documentation
Role in this project:Technical Writer
Technical WriterContributions:9 commits, 1 PR, 11 comments in 13 days
Contributions summary:Marianne primarily contributed to the repository by creating and updating documentation, specifically within the "site/en/tutorials" directory. Their work included adding a new tutorial on text generation using a RNN with eager execution, and modifying other documentation pages. Further contributions included updating image URLs and other minor edits, all related to improving the quality and clarity of the TensorFlow documentation.
deep-learningmachine-learningdeep-neural-networkstensorflow-tutorialstensorflow
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.