Mathieu Tuli
Machine Learning Engineer at Meta
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolMathieu Tuli is a Machine Learning Engineer with nine years of experience, a Master’s in Computer Science from the University of Toronto, and a track record of turning research into production-ready systems. He has published state-of-the-art work in adaptive optimizers and neural architecture search (NeurIPS, CVPR, ICCV) and transitioned those skills into industry roles building generative and video-language models at Meta and LG Electronics. Mathieu combines deep research chops with practical product delivery—shipping multi-camera perception systems and end-to-end training pipelines—and has contributed to notable open-source projects like Microsoft’s TextWorld by improving core game logic for RL agents. Based in Toronto, he thrives at the intersection of optimization, 3D generative AI, and applied reinforcement learning, and enjoys solving real-world problems by moving models from prototype to scalable deployment.
9 years of coding experience
7 years of employment as a software developer
High School, High School at Asbury College (Secondary School)Masters, Computer Science | AI, 4.0/4.0, Masters, Computer Science | AI, 4.0/4.0 at University of Toronto
English, French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
text-based10
python10
reinforcement-learning10
data-structure9
type-checking9
data-structures9
typehinting9
Programming languages (6)
Github contributions (5)
microsoft/TextWorld
Aug 2021 - Sep 2021
TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 1 PR, 3 comments in 1 day
Contributions summary:Mathieu primarily worked on enhancing the TextWorld environment's game logic and data structures. They fixed issues with the handling of win/fail conditions by modifying game properties and integrating them into the game state. The changes included renaming functions, adding new properties for fail conditions, and improving type hinting to ensure data integrity within the game framework. These adjustments involved changes to the core game logic and wrapping of the environment to expose new information about winning and failing conditions.
game-aigamesrl-agentsreinforcement-learningevaluation
MathieuTuli/.dotfiles
Mar 2019 - Apr 2025
Contributions:402 pushes, 12 branches, 1 tag in 6 years 1 month
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