Remi Cadene is a founder and AI scientist who builds embodied intelligence—now leading UMA as Co-Founder and CEO after scaling real-world AI at Tesla Autopilot and launching foundational robot learning work like LeRobot at Hugging Face. With a PhD from Sorbonne and postdoctoral work at Brown, he blends rigorous research on neural architectures, explainability and human behavior modeling with production experience shipping neural nets for Tesla Optimus. He has 11+ years of hands-on ML engineering and MLOps, contributing widely to open-source projects such as pretrained-models.pytorch and the popular LeRobot effort that makes end-to-end robot learning more accessible. Remi’s background spans vision-and-language research at Facebook AI to core data and feature-engineering work in VQA systems, reflecting both theoretical depth and pragmatic system-building. Based in Paris, he draws inspiration from neuroscience and a commitment to open source to push robotics from lab demos toward everyday quality-of-life impact.
11 years of coding experience
6 years of employment as a software developer
Licence 3, Economics and Financial Management, Licence 3, Economics and Financial Management at Université Paris Cité
Licence 1&2, Economics and Financial Management, Licence 1&2, Economics and Financial Management at Paris-Sorbonne University Abu Dhabi
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Sorbonne University
Master's degree, Computer Science, Master's degree, Computer Science at Pierre and Marie Curie University
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
Role in this project:
ML Engineer
Contributions:117 commits, 26 PRs, 90 pushes in 2 years
Contributions summary:Remi primarily contributed to the development and implementation of pretrained models within the PyTorch framework. They added support for various ResNeXt architectures, Inception-based models (including InceptionResNetV2 and InceptionV4), and DualPathNetworks. They also improved the API for several models and introduced a toy-example demonstrating the usage of these pretrained models, indicating a focus on model integration and usability. Moreover, the user added support for pre-trained weights of NASNet-A-Large, further demonstrating proficiency in integrating and utilizing advanced convolutional neural network architectures.
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
Role in this project:
Full-stack Developer
Contributions:430 reviews, 130 PRs, 550 pushes in 11 months
Contributions summary:Remi implemented initial setup for the LeRobot project, setting up the structure and defining the project's name, description, and other metadata. They then added the `simxarm` environment with `torchrl`, indicating a focus on reinforcement learning for robotics. They also contributed to the evaluation script to support testing the AI agent on this environment.
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