Morgan Giraud
AI Research Engineer at Kog
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolMorgan Giraud is an AI research engineer based in Paris with 13 years of experience blending deep learning research and full-stack web engineering. Skilled in Python (TensorFlow, PyTorch, JAX) and JS/TS, he trains and interprets deep networks while building web tools and deployments that surround ML models. He has led engineering teams and startups as CTO/co-founder of Explee and technical lead at World of Women, proving he can move projects from prototype to production. His open-source work includes contributions to PySyft and privacy-preserving ML libraries (tf-encrypted), demonstrating expertise in model serialization, ONNX export, and secure ML workflows. Comfortable in both academic-style research projects (grounded language learning, RL, NLP) and product engineering, he’s actively seeking a longer-term, research-focused role. A lesser-known strength is his knack for bridging low-level model internals with user-facing applications, making complex models practical and explainable.
13 years of coding experience
12 years of employment as a software developer
Engineer's degree, International Relations and Affairs, Engineer's degree, International Relations and Affairs at Institut national des TélécommunicationsAssociate’s Degree, Mathématiques et physiques, Associate’s Degree, Mathématiques et physiques at Lycée Berthollet
French, English, Italian
Stackoverflow
41reputation
1kreached
1answer
2questions
Github Skills (12)
pytorch10
data-serialization10
python10
federated-learning10
serialization10
cryptography9
onnx9
deeplearning-ai9
deep-learning9
json8
flowtype6
tensorflow6
Programming languages (11)
Github contributions (5)
OpenMined/PySyft
Jan 2018 - Mar 2018
Perform data science on data that remains in someone else's server
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 1 PR in 2 months
Contributions summary:Morgan's contributions primarily involve adding functionality to the `pysyft` library. They implemented a `load` function and an `export` function with an helper `export_as_json` function, indicating work on data serialization and model persistence. The user also worked on a first graph topology serialization, demonstrating involvement in model representation and potentially a part of the model serving pipeline using JSON. Furthermore, they added an ONNX export function indicating integration with ONNX.
pytorchcryptographyacquiringpythonscience
morgangiraud/mps
Feb 2017 - Aug 2023
A markov process simulator: play with markov process, markov reward process and markov decision process
Contributions:2 PRs, 23 pushes, 6 branches in 6 years 7 months
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