William Grisaitis
Machine Learning Engineer at Qevlar AI
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolWilliam Grisaitis is a machine learning engineer with 11 years of experience blending research-grade ML, product-focused development, and MLOps. Based in Paris, he has applied deep learning to vision and signal problems across domains from connectomics at HHMI to precision oncology at Dana-Farber and industry work in finance, gaming, and edge inference. He’s comfortable shipping end-to-end systems—training custom models, building reproducible Docker/GCP pipelines, and deploying models for production or in-browser inference. His research revealed concrete limitations in popular deconvolution methods and produced a rigorous evaluation and synthetic-data framework that improved how teams interpret results. An active contributor to community projects, he has improved reproducibility and deployment for notable repos like Hardmaru’s write-rnn TensorFlow implementation. He combines a physics undergrad and ongoing graduate math work with a pragmatic, product-minded approach to hard ML problems.
11 years of coding experience
8 years of employment as a software developer
A.B. Physics, A.B. Physics at Duke UniversityM.S. Mathematics, M.S. Mathematics at University of Central Florida
French, Spanish
Stackoverflow
5,601reputation
3.3mreached
56answers
48questions
pandas
top-5%
csv
top-1%
python
top-5%
Github Skills (21)
docker10
python10
dockers10
tensorflow10
csv10
pandas9
machine-learning9
cicd8
n7
autopep87
rnn-model7
deep-learning7
cuda6
variance6
process-monitoring6
Programming languages (19)
Github contributions (5)
hardmaru/write-rnn-tensorflow
Feb 2018 - Feb 2018
Generative Handwriting using LSTM Mixture Density Network with TensorFlow
Role in this project:
MLOps Engineer
MLOps EngineerContributions:5 commits, 4 PRs, 4 comments in 4 days
Contributions summary:William contributed to the project by improving the development workflow and model deployment. This includes creating a Docker image and run script, facilitating easier execution and reproducibility of the project. The user also addressed code formatting standards and performed minor code refactoring to improve readability, which aids in the overall maintainability of the project. Additionally, renaming variables to improve clarity indicates an effort to refine the code base.
lstmtensorflow
TuragaLab/PyGreentea
Jan 2016 - Jul 2019
Contributions:7 releases, 138 pushes, 48 branches in 3 years 5 months
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