Olivier Moindrot
Product Manager at Sierra
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolOlivier Moindrot is a product-minded AI leader with a decade of experience building ML-driven products and scaling ops teams, currently shaping agent-based products at Sierra. He co-founded Fragment (YC S23) to marry automation with human workflows, reflecting hands-on experience managing 300+ offshore operators at MetaMap while leading an AI team. Trained at École Polytechnique and Stanford in CS and AI, he has a strong research-to-production track record from Owkin’s biomedical ML to teaching and open-source contributions for Stanford courses. His engineering roots include efficient TensorFlow implementations (notably triplet loss and CS230 examples) and practical data-pipeline refactors that accelerated model training. Comfortable across research, product, and ops, he focuses on making AI augment human work rather than replace it. Based in Paris, he blends startup founder grit with academic rigor to build scalable, human-centric systems.
10 years of coding experience
7 years of employment as a software developer
Diplôme d'ingénieur (M.S), Computer Science & Entrepreneurship, Diplôme d'ingénieur (M.S), Computer Science & Entrepreneurship at École PolytechniquePreparatory Program, MP*, Mathematics, Preparatory Program, MP*, Mathematics at Lycée Sainte-GenevièveMaster of Science - MS, Computational & Mathematical Engineering, Master of Science - MS, Computational & Mathematical Engineering at Stanford University
French, English, Spanish
Stackoverflow
28,128reputation
1.9mreached
210answers
0questions
tensorflow
top-1%
python
top-1%
deep-learning
top-1%
machine-learning
top-1%
data-visualization
top-1%
neural-network
top-1%
Github Skills (28)
pytorch10
python10
evaluation10
machine-learning10
mask-rcnn10
eval10
deep-learning10
trainings10
tensorflow10
mnist10
neural-network10
computer-vision10
faster-rcnn10
data-visualization10
modeling10
Programming languages (7)
Github contributions (5)
omoindrot/tensorflow-triplet-loss
Mar 2018 - Jan 2019
Implementation of triplet loss in TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 4 PRs, 25 pushes in 10 months
Contributions summary:Olivier implemented a convolutional neural network (CNN) model for the Fashion MNIST dataset. They added triplet loss and embedding visualization capabilities, modifying the model definition to output embeddings and utilizing TensorFlow's projector for visualizing the results. Furthermore, they refactored the data pipeline by using a fixed-length record dataset.
triplet-lossdeep-learningtripletlosstensorflow
cs230-stanford/cs230-code-examples
Jan 2018 - Feb 2018
Code examples in pyTorch and Tensorflow for CS230
Role in this project:ML Engineer
ML EngineerContributions:33 commits, 2 PRs, 30 pushes in 1 month
Contributions summary:Olivier contributed to the development of an MNIST training example within a PyTorch and TensorFlow code repository for CS230. Their work involved setting up the model definition, the training loop, and the evaluation processes. The user made modifications to the model structure, data pipelines, and training configurations. The user's primary focus appears to be on establishing a working example for training a model.
pytorchdeep-learningcomputer-visiontensorflownatural-language-processing
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