Hassan Eslami
Principal Software Engineer at NVIDIA
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolHassan Eslami is a Principal Software Engineer based in Seattle with a decade of experience designing and scaling distributed ML and data infrastructure. He has led distributed training and ML platform efforts at Facebook and Snap and now drives AI systems at NVIDIA, blending deep research (PhD-level work) with production-grade engineering. His open-source contributions to projects like Caffe2 and Apache Giraph show a practical focus on performance, stability, and memory-efficient distributed algorithms—work that tuned parameter servers and enabled out-of-core messaging for large-scale workloads. Colleagues rely on him for technical leadership across multi-threading, resource utilization, and system-level optimizations that turn research prototypes into robust platforms.
10 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at University of Illinois Urbana-ChampaignBachelor of Science (BSc) Computer Engineering, Bachelor of Science (BSc) Computer Engineering at Sharif University of Technology
Github Skills (20)
caffe10
distributed-training10
python10
memory-management10
big-data10
machine-learning10
java10
giraph10
javas10
deep-learning10
performance-optimization10
data-structure9
artificial-intelligence9
algorithm9
algorithms9
Programming languages (5)
Github contributions (5)
apache/giraph
Jun 2015 - Jul 2016
Mirror of Apache Giraph
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 commits, 3 PRs, 12 comments in 1 year 1 month
Contributions summary:Hassan primarily contributed to improving the Apache Giraph project's core back-end functionality and performance. Their work involved enhancing the PartitionStore API to optimize statistics operations, refactoring vertex mutation mechanisms for multi-threading and improved performance, and adding out-of-core messages for the adaptive out-of-core mechanism, improving the overall memory management of the system. The user also focused on fixing bugs related to the core framework, which shows the user's responsibility for core maintenance.
javagiraphapachebig-data
facebookarchive/caffe2
Aug 2017 - Dec 2017
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:ML Engineer
ML EngineerContributions:28 commits, 1 PR in 3 months
Contributions summary:Hassan's commits primarily focused on optimizing the Caffe2 deep learning framework. They implemented monitoring of CPU and network utilization to tune the number of parameter servers. Furthermore, the user refactored parameter initialization logic and tagged sparse parameters for a distributed training framework, demonstrating contributions to core model training functionalities. They also addressed type inference issues and improved the net-rewriting pipeline, indicating a focus on performance and stability.
pytorchscalablecaffe2deep-learningml
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