Magdy Saleh
Senior Machine Learning Engineer at Predibase
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolMagdy Saleh is a Senior Machine Learning Engineer based in New York with a decade of experience building scalable ML systems and production-ready model training workflows. Currently at Predibase, he focuses on low-code ML platforms and distributed training, having contributed to the Ludwig framework to improve progress reporting in Ray-based distributed training. His background spans applied ML roles at Gridspace and academic instruction at Stanford (CS221, CS103), grounded in an MS in Computer Science from Stanford and a BE in Biomedical Engineering from UCL. Magdy blends research-minded rigor with practical engineering, evidenced by work on tooling that makes large-scale model training more observable and reliable. He brings cross-domain experience from finance internships to biomedical simulation research, giving him a pragmatic, systems-oriented perspective on ML problems.
10 years of coding experience
3 years of employment as a software developer
University College LondonCairo American CollegeMaster of Science Computer Science, Master of Science Computer Science at Stanford University
English, Arabic, German
Stackoverflow
1reputation
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Github Skills (9)
pytorch10
machine-learning10
deeplearning-ai10
ray10
deep-learning10
python10
llm9
mlops8
natural-language-processing8
Programming languages (2)
Github contributions (5)
ludwig-ai/ludwig
Jun 2022 - Sep 2022
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:
ML Engineer
ML EngineerContributions:22 reviews, 10 commits, 18 PRs in 3 months
Contributions summary:Magdy contributed to the improvement of progress bar functionality within the Ludwig framework, focusing on integrating and reporting progress in distributed training environments, particularly with Ray. Code changes indicate modifications to the `Predictor` and `Trainer` classes, enhancing their ability to display progress updates. The commits demonstrate work related to progress bar configuration and integration with Ray's distributed computing environment, which is critical for training large-scale machine learning models.
fairness-mlpythonframework-learningdeep-learning-frameworknatural-language-processing
magdyksaleh/cs224w_ssm
Oct 2018 - Dec 2018
Contributions:36 commits, 1 push in 1 month
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