Mark Daoust
Software Engineer at Google DeepMind
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMark Daoust is a seasoned software engineer with 16 years of experience blending ML engineering, developer tooling, and technical writing, now at Google DeepMind after seven years in developer programs at Google. He specializes in improving developer experience across flagship ML projects—making tangible contributions to TensorFlow, Keras, TensorBoard and related ecosystems by automating API docs, refining tutorials, and modernizing Colab examples. Comfortable across back-end, full-stack and DevOps tasks, he has a track record of fixing hard-to-spot issues (doc generation, parser bugs, dependency pinning) that keep large open-source projects usable at scale. His background includes systems engineering at CAE and leadership experience as a Canadian Forces sergeant, giving him a disciplined, pragmatic approach to complex problems. Based in Palo Alto with a Mechanical Engineering degree from Concordia, he combines engineering rigor with a focus on clear, accessible documentation that helps ML users and contributors move faster.
16 years of coding experience
17 years of employment as a software developer
Bachelor's degree, Mechanical Engineering, Bachelor's degree, Mechanical Engineering at Concordia University
Stackoverflow
6,347reputation
533kreached
91answers
2questions
machine-learning
top-5%
tensorflow
top-1%
keras
top-5%
nlp
top-5%
python
top-5%
neural-network
top-1%
Github Skills (80)
notebook10
python10
datasets10
tensorflow-lite10
deep-learning10
text-processing10
tokenize10
develop10
ai-agent10
user-manual10
basics10
numpy10
tokenizer10
localization10
api10
Programming languages (15)
Github contributions (5)
tensorflow/docs
Mar 2017 - Jan 2023
TensorFlow documentation
Role in this project:
Technical Writer
Technical WriterContributions:1 release, 365 reviews, 1389 commits in 5 years 11 months
Contributions summary:Mark primarily contributed to the project by adding and modifying documentation related to the TensorFlow project. The changes focused on adding details on different aspects of the project, including descriptions of the dataset, model architecture, training methods, and model evaluation, improving the clarity of the documentation. Further, image attribution was also added to one of the tutorial.
deep-learningmachine-learningdeep-neural-networkstensorflow-tutorialstensorflow
googlecodelabs/tensorflow-for-poets-2
May 2017 - Apr 2019
Role in this project:ML Engineer & Mobile Developer
ML Engineer & Mobile DeveloperContributions:47 commits, 2 PRs, 44 pushes in 1 year 11 months
Contributions summary:Mark primarily contributed to the adaptation and integration of a TensorFlow Lite model within an iOS application. They updated the iOS demo, converted the model, and adjusted the code to use a non-quantized MobileNet model with the photos library. Additionally, they modified the Android application, and scripts for retraining.
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