Alex Hyder
Director Of Engineering - DRIVE Sim at NVIDIA
Los Gatos, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Hyder is a seasoned engineering leader who blends deep graphics and simulation expertise with cross-industry delivery—from NASA spaceflight simulators to AAA games and NVIDIA’s DRIVE Sim. With decades leading teams at Electronic Arts, Presagis, and NVIDIA Lightspeed, he has shipped high-performance 3D engines, remastered iconic titles, and integrated emerging tech like LiDAR and photogrammetry into production pipelines. He pairs hands-on systems work (CUDA kernel and NCCL/UCX contributions to RAPIDS graph and ML primitives) with strategic product leadership, making him fluent in both low-level performance optimization and studio-scale operations. Based in Los Gatos, he is known for turning demanding simulation requirements into scalable software and for pivoting teams between creative game development and mission-critical engineering applications.
7 years of coding experience
21 years of employment as a software developer
M. Eng Mechanical Engineering, M. Eng Mechanical Engineering at McGill University
English, French
Github Skills (24)
algorithm10
algorithms10
graph-algorithms10
c-language10
python10
data-engineering10
graph10
data-structure10
gpu10
ccl10
cuda10
data-structures10
cprogramming-language10
data-pipelines9
machine-learning9
Programming languages (5)
Github contributions (5)
rapidsai/cugraph
Dec 2018 - Sep 2021
cuGraph - RAPIDS Graph Analytics Library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:213 reviews, 720 commits, 261 PRs in 2 years 9 months
Contributions summary:Alex contributed to kernel implementations, including adding and fixing conversion behaviors, which indicates a strong focus on core functionalities and underlying system logic. The commits show modifications to cugraph.cu and graph_utils.cuh, suggesting work on improving the graph processing algorithms and utility functions of the library. Furthermore, the implementation of edge list features reveals a commitment to expanding the library's data structure capabilities.
cudagraph-analysisanalyticsgraph-analyticsgraphml
rapidsai/raft
Jun 2020 - Feb 2021
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Role in this project:Back-end Developer / ML Engineer
Back-end Developer / ML EngineerContributions:31 reviews, 13 commits, 7 PRs in 8 months
Contributions summary:Alex's contributions center around the development of core algorithms and infrastructure within the `raft` repository, which focuses on high-performance machine learning and information retrieval. They implemented and refined the Minimum Spanning Tree (MST) solver, including kernel development. The user also worked on communication primitives, particularly within the `raft/comms` module, using NCCL and UCX. These efforts included code changes to enhance performance and add features for collective and point-to-point communications.
sciencedata-sciencemachine-learninggraph-data-sciencefundamental
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.