Abdullah Rashwan
Senior Staff Research Engineer at Google DeepMind
United States
Summary
🤩
Rockstar🎓
Top SchoolAbdullah Rashwan is a Senior Staff Research Engineer with nine years of experience building and productionizing large-scale ML systems across Google, Adobe, and DeepMind. He combines a PhD in AI from the University of Waterloo with hands-on engineering roles at Google Brain and Google Cloud AI, focusing on transformer-based NLP and vision models as well as media generation. Abdullah has contributed nontrivially to the widely used tensorflow/models repository—improving data pipelines, optimization, metrics, and model serving—which underscores his ability to bridge research prototypes and reliable serving infrastructure. He has led research engineering efforts in both startups and industry teams, blending deep modeling expertise with pragmatic software engineering to ship scalable ML products. Known for tackling cross-cutting problems from training loops to export/serving, he brings a track record of turning advanced research into production-ready systems.
9 years of coding experience
12 years of employment as a software developer
Bachelor of Engineering (B.E.) Electrical and Computer Engineering, Bachelor of Engineering (B.E.) Electrical and Computer Engineering at Cairo UniversityDoctor of Philosophy (Ph.D.) Artificial Intelligence, Doctor of Philosophy (Ph.D.) Artificial Intelligence at University of Waterloo
English, Arabic
Github Skills (9)
machine-learning10
deep-learning10
tensorflow10
data-pipeline10
data-pipelines10
model-optimization9
computer-vision9
api8
apidoc8
Programming languages (2)
Github contributions (5)
tensorflow/models
May 2020 - Jan 2023
Models and examples built with TensorFlow
Role in this project:
Back-end & ML Engineer
Back-end & ML EngineerContributions:1 release, 162 reviews, 230 commits in 2 years 9 months
Contributions summary:Abdullah made numerous internal changes to the `tensorflow/models` repository, specifically within the `official/nlp` and `official/vision` directories, indicating involvement in machine learning model development. They modified data pipelines for transformer models and incorporated features, along with changes to optimization, metric calculations, and training loops. The user also made changes to serve and export functions for the machine learning models in serving environments.
deep-learningtensorflow
arashwan/BMM
Feb 2017 - Feb 2017
Contributions:2 pushes, 1 branch in 5 days
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