Rayan Hatout
Machine Learning Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolRayan Hatout is a Machine Learning Engineer at Meta in New York with seven years of experience building recommendation and ranking systems for Instagram Reels, Explore, and Accounts You May Follow. He holds a first-class MEng in Computing with a specialization in AI and ML from Imperial College London and combines strong academic foundations with hands-on production experience. At Meta he focuses on large-scale personalization and ranking challenges, bringing empirical rigor from simulation-based testing and predictive modeling internships earlier in his career. Rayan is also an active open-source contributor to tinygrad, where he optimized core symbolic computation, ShapeTracker, and tensor operations—demonstrating a knack for squeezing performance from compact ML frameworks. Colleagues describe him as both a pragmatic engineer who ships robust systems and a tinkerer who improves low-level ML tooling.
7 years of coding experience
1 year of employment as a software developer
Master of Engineering - MEng Computing - Artificial Intelligence and Machine Learning, Master of Engineering - MEng Computing - Artificial Intelligence and Machine Learning at Imperial College LondonHigh School Diploma General Studies, High School Diploma General Studies at Lycée Lyautey au Maroc
English, French, Arabic
Stackoverflow
640reputation
67kreached
31answers
9questions
Github Skills (21)
pytorch10
operation10
python10
tensorrt10
machine-learning10
tensorflow10
tensor10
optimization10
data-structure9
algorithm9
algorithms9
numpy9
data-structures9
nlp6
oauth6
Programming languages (10)
Github contributions (5)
tinygrad/tinygrad
May 2023 - Jul 2023
You like pytorch? You like micrograd? You love tinygrad! ❤️
Role in this project:
ML Engineer
ML EngineerContributions:14 reviews, 10 PRs, 69 comments in 1 month
Contributions summary:Rayan's contributions center around optimizing and improving the `tinygrad` library, which is focused on machine learning. Their work includes optimizations within the core symbolic computation engine, as well as improvements to the ShapeTracker and Tensor modules. These changes involved modifications to the underlying data structures and algorithms, aiming to improve efficiency and performance. Additionally, they made significant improvements to the LLaMa complex multiplication, and refactored several functions to improve efficiency.
deep-learningpytorchmicrograd
rayanht/paprika
Feb 2021 - Mar 2021
Paprika is a python library that reduces boilerplate. Heavily inspired by Project Lombok.
Contributions:4 releases, 11 reviews, 31 commits in 9 days
python-librarypythonboilerplateproject-lomboklombok
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