Sami Kama
Chief Executive Officer at C-Gen.AI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolSami Kama is a seasoned engineering leader and CEO based in the San Francisco Bay Area with a decade of professional experience building high-performance systems across AI, cloud, and scientific computing. He has led deep learning and MLOps efforts at Stability AI and Amazon and drove GPU-optimized model operations at NVIDIA, combining production-grade engineering with research rigor from a PhD in high energy physics. His early career at CERN and as a research faculty member gave him deep expertise in real-time distributed data acquisition and large-scale trigger farms processing hundreds of GB/s, a background that informs his approach to scalable ML infrastructure. An active contributor to ML compilers and recommendation frameworks, he has improved backend architecture in the openxla XLA compiler and implemented GPU-accelerated ops in DeepRec, signaling hands-on competence with low-level performance optimization. As CEO of C-Gen.AI he bridges technical depth and product leadership, translating complex system-level improvements into business outcomes. Colleagues describe him as someone who moves fluidly between instruction-level optimization and executive strategy, a rare combination that accelerates both research and production deployment.
10 years of coding experience
22 years of employment as a software developer
Doctor of Philosophy (Ph.D.), High Energy Physics, Doctor of Philosophy (Ph.D.), High Energy Physics at Humboldt University of BerlinMaster of Science (M.Sc.), High Energy Physics, Master of Science (M.Sc.), High Energy Physics at Orta Doğu Teknik Üniversitesi / Middle East Technical University
English, Turkish, German
Github Skills (16)
cuda10
xla10
gpu-programming10
machine-learning10
compiler-design10
device-management10
c-language10
deep-learning10
tensorflow10
cprogramming-language10
back-end-development10
computer-vision8
python8
scalability7
recommendation-engine7
Programming languages (10)
Github contributions (5)
DeepRec-AI/DeepRec
May 2019 - Jan 2020
DeepRec is a high-performance recommendation deep learning framework based on TensorFlow. It is hosted in incubation in LF AI & Data Foundation.
Role in this project:
ML Engineer
ML EngineerContributions:18 commits in 7 months
Contributions summary:Sami's contributions focused on implementing and modifying GPU-accelerated operations within the TensorFlow framework. Their work included adding a new operation, `GenerateBoxProposals`, and addressing review comments to improve its functionality. The commits also involved refactoring and optimizing existing code, such as the `CombinedNonMaxSuppression` op, to leverage GPU capabilities effectively. These changes demonstrate a focus on enhancing the performance of the deep learning framework.
pythondeep-learningrecommendationdistributed-trainingmachine-learning
openxla/xla
Apr 2018 - Dec 2018
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 commits in 8 months
Contributions summary:Sami primarily focused on refactoring and enhancing the backend infrastructure of the XLA compiler. Their contributions included replacing data structures like tuples with classes to improve code maintainability, as well as modifying device handling and stream executor initialization within the backend. The changes involved alterations to core components, including client libraries, service configurations, and platform utilities, indicating a strong understanding of the system's architecture. Furthermore, they addressed code formatting issues and improved include ordering.
compilercommunity-drivenmachine-learningmodular
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