Alexandre Vassalotti
Software Engineer at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolAlexandre Vassalotti is a software engineer with 19 years of experience specializing in distributed systems, databases, programming languages, and system optimization, currently working on fleetwide observability at Google in Mountain View. He spent nearly a decade on Search Ads SRE and now applies that operational rigor to large-scale telemetry and reliability challenges. An active open-source contributor, Alexandre has improved core Python tooling—working on setuptools and optimizing CPython’s pickle implementation—and helped modernize reinforcement learning tooling in Google’s Dopamine framework. He blends low-level performance tuning with pragmatic backward-compatibility fixes, demonstrating a knack for making complex systems both faster and more robust.
18 years of coding experience
BSc, Computer Science, BSc, Computer Science at McGill University
French, English
Stackoverflow
1,885reputation
350kreached
3answers
1question
django
top-5%
python
top-5%
Github Skills (24)
python10
machine-learning10
reinforcement-learning10
ml10
distutils10
tensorflow10
performance-optimization10
ai10
configparser10
backward-compatibility9
c119
django9
c179
testing7
algorithm7
Programming languages (4)
Github contributions (5)
google/dopamine
Mar 2020 - Jul 2020
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Role in this project:
ML Engineer
ML EngineerContributions:49 commits in 4 months
Contributions summary:Alexandre primarily focused on migrating code to the TensorFlow 2 native API, specifically within the `replay_memory` module, indicating a focus on core reinforcement learning framework components. They also addressed test setup issues by adding missing `super().setUp()` calls, and replaced `tf.logging` with `absl.logging` to modernize the codebase. Furthermore, they made changes to the `run_experiment.py` file, indicating involvement in the execution and configuration aspects of the project.
reinforcement-learning-algorithmsfast-prototypinggooglereinforcement-learningprototyping
idobatter/cpython
Nov 2013 - Dec 2013
Semi-official read-only mirror of the CPython Mercurial repository
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:34 commits in 8 days
Contributions summary:Alexandre primarily focused on optimizing the performance of the CPython's pickle module. Their contributions included making framing optional, improving the efficiency of the pickle protocol 4, and removing redundant code paths. The user also made changes to improve the pickling of built-in methods and singletons. Additionally, they addressed compiler warnings and refactored code related to fast calls within the pickle module.
pythonmercurialsemicpythonread-only
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