Mostafa Badawy
Senior Machine Learning Engineer at Apple
Barcelona, Catalonia, Spain
Summary
👤
Senior🎓
Top SchoolMostafa Badawy is a Senior Machine Learning Engineer with 9 years of experience designing, building, and shipping production ML systems across NLP and computer vision, currently contributing to Siri’s Agentic AI and retrieval-augmented generation at Apple. He combines deep research credentials—multiple papers at NeurIPS, CVPR, IROS and ICIP and authorship of the ShuffleSeg segmentation work—with hands-on engineering that moves models from prototype to scalable deployment. His career spans startups and industry R&D, delivering products such as Artville.ai, automated background-removal pipelines, and an ML matching platform for job recommendations. An active open-source contributor, he has refactored core convolutional layers in popular segmentation codebases to improve clarity and robustness. Fluent in Arabic, English and Spanish and based in Barcelona, he blends academic rigor with practical system design and mentoring experience. Colleagues note his knack for simplifying complex model stacks into maintainable, deployment-ready pipelines.
9 years of coding experience
5 years of employment as a software developer
Bachelor's degree Computer Engineering, Bachelor's degree Computer Engineering at Cairo University
English, Arabic, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
neural-network10
model-building10
convolutional-neural-networks10
tensorflow10
python10
modeling10
model-driven10
model-driven-development10
machine-learning9
deep-learning9
Programming languages (3)
Github contributions (5)
MSiam/TFSegmentation
Jul 2017 - Jun 2018
RTSeg: Real-time Semantic Segmentation Comparative Study
Role in this project:
Back-end Developer
Back-end DeveloperContributions:197 commits, 47 pushes, 12 comments in 10 months
Contributions summary:Mostafa modified and refactored the convolutional layers within the `tfsegmentation` repository. They merged redundant layers and fixed associated bugs, updating the codebase to be more concise. Further, they moved helper functions related to pretrained weights to their proper locations, simplifying the code and updating conventions. The changes involved modifications to the `layers/convolution.py`, `layers/utils.py`, `models/encoders/VGG.py`, `layers/pooling.py`, `models/encoders/mobilenet.py`, and `layers/dense.py` files.
pytorchsegmentationsemantic-segmentationcomparativereal-time-semantic-segmentation
MG2033/MobileNet-V2
Jan 2018 - Feb 2018
Contributions:69 commits, 1 PR, 12 pushes in 9 days
deep-learningpytorchmobilenet-v2mobilenet
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