Emile Contal
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolEmile Contal is a machine-learning-focused technologist and entrepreneur with 13 years of experience building recommendation systems and production AI products, most notably as Co-Founder and CTO of Crossing Minds. He holds a PhD in Statistical Learning from ENS Paris-Saclay and has published theoretical contributions in Bayesian optimization and Gaussian processes while applying them to real-world problems from hyperparameter tuning to renewable energy. Now a Member of Technical Staff at OpenAI in San Francisco, he combines deep research instincts with hands-on engineering, having also contributed QA and test automation improvements to the widely used CuPy GPU-backed numerical library. At StartX he supports founders as an Admission Judge and Neighborhood Lead, reflecting his commitment to the Bay Area startup ecosystem. Known for bridging rigorous theory and practical systems, he brings a rare mix of academic depth, startup leadership, and production-grade ML engineering.
13 years of coding experience
9 years of employment as a software developer
Master's degree Theoretical Computer Science, Master's degree Theoretical Computer Science at Ecole Normale Supérieure de CachanLicence Mathematics and Computer Science, Licence Mathematics and Computer Science at UVSQ Université de Versailles Saint-Quentin-en-Yvelines
French, English
Github Skills (9)
unit-testing10
scipy10
python10
cupy10
numpy10
testing10
gpu7
cuda7
cublas5
Programming languages (10)
Github contributions (5)
cupy/cupy
Jul 2019 - Sep 2019
NumPy & SciPy for GPU
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:9 commits, 2 PRs, 15 comments in 2 months
Contributions summary:Emile contributed significantly to the testing infrastructure of the CuPy library, specifically focusing on the `scipy.linalg` module. Their work involved adding reconstruction tests for the `lu_factor` function, which ensures the correctness of the LU decomposition. They also addressed documentation issues and improved the readability of the tests. Additionally, the user made changes to address the behavior of sorting functions.
cudapythoncusolvergpunumpy
rux-pizza/poker-ai
Jan 2013 - Apr 2013
Contributions:38 commits in 2 months
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