Amjad Almahairi
Staff Research Scientist at Google
California, United States
Summary
👤
Senior🎓
Top SchoolAmjad Almahairi is a Staff Research Scientist in California with 12 years of deep learning experience spanning generative AI, LLMs, NLU, computer vision and multimodal systems. He has driven production-scale research at Meta AI (contributing to Llama 2 safety, WPIE embeddings and MultiRay) and built cost-saving inference infrastructure at Anyscale (an LLM Router that cut costs by 85% with 95% quality retention, ICLR 2025). Now at Google he focuses on enterprise AI agents, combining rigorous research with deployment experience. His open-source contributions to foundational libraries like Theano and pylearn2 demonstrate long-standing commitments to tooling, documentation and reproducible examples. Trained at MILA with a PhD in deep learning, he blends academic rigor with pragmatic engineering to ship scalable, safety-conscious AI systems.
12 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD Deep Learning, Doctor of Philosophy - PhD Deep Learning at Université de MontréalBEng. Informatics Artificial Intelligence, BEng. Informatics Artificial Intelligence at Damascus UniversityMSc. Computer Science, MSc. Computer Science at McGill University
Github Skills (11)
unit-testing10
machine-learning10
python10
profiler10
theano10
profiling10
documentation10
jupyter-notebook9
accelerated-computing8
gpu8
gpgpu8
Programming languages (2)
Github contributions (5)
Theano/Theano
Sep 2014 - Jan 2017
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Role in this project:
Back-end Developer
Back-end DeveloperContributions:118 commits, 15 PRs, 43 comments in 2 years 4 months
Contributions summary:Amjad primarily contributed to improving and extending the documentation and profiling tools within the Theano library. They updated existing tutorials, adding profiling outputs and modifying the guidance for CPU and GPU computations. Additionally, the user made various code adjustments, including adding support for Python 3.x and correcting minor typos to refine the documentation and usability of the library's features.
mathematical-expressionsmultidimensional-arrayspymcpythontheano
lisa-lab/pylearn2
Feb 2014 - Mar 2015
Warning: This project does not have any current developer. See bellow.
Role in this project:ML Engineer
ML EngineerContributions:29 commits, 1 PR, 1 comment in 1 year 1 month
Contributions summary:Amjad primarily contributed to unit testing and updating the example tutorial for the softmax regression model within the `pylearn2` repository. Their work involved adding test cases to verify the functionality of the tutorial notebook and ensuring the output was up to date. Further commits modified the way the project accesses the tutorials folder and ensures that all cells within the notebook are up to date.
javascripttypescript
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