Theophile Gervet
Co-Founder at Genesis AI
Pittsburgh, Pennsylvania, France
Summary
🤩
Rockstar🎓
Top SchoolTheophile Gervet is an AI researcher and founder with a decade of experience building next‑generation machine learning systems, currently co-founding Genesis AI after research roles at Mistral AI and Meta AI. He holds advanced degrees from Carnegie Mellon and McGill and completed a PhD focused on reinforcement learning, robotics, and computer vision, applying those techniques to real-world robot navigation and education problems. As an early engineer and later ML lead at Relyance, he scaled core NLP and model infrastructure to deliver privacy-focused products to customers like Zoom and Coinbase. Theophile blends deep research rigor with hands-on product and team building—moving from protoype robotics research to shipping ML systems in production—and often operates at the intersection of open research and developer-facing engineering. An international profile based in Pittsburgh with roots in France, he favors projects that translate cutting‑edge models into practical, scalable applications.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Carnegie Mellon UniversityBachelor of Science Honours Computer Science & Math, Bachelor of Science Honours Computer Science & Math at McGill University
Github Skills (121)
python10
simulator10
deep-learning10
gpu10
gpu-acceleration10
robotics10
modular10
architecture10
acceleration10
numpy10
ai10
neural-network10
deep-reinforcement-learning10
computer-vision10
artificial-intelligence10
Programming languages (9)
Github contributions (5)
theophilegervet/social_navigation
May 2023 - Aug 2023
A modular high-level library to train embodied AI agents across a variety of tasks and environments.
Contributions:2 PRs, 188 pushes, 1 branch in 2 months
theophilegervet/learner-performance-prediction
Feb 2020 - May 2022
Simple and performant implementations of learner performance prediction algorithms.
Contributions:9 commits, 95 pushes, 2 branches in 2 years 3 months
implementationspredictionmachine-learningperformanceperformant
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