William Tambellini
Senior Team Manager at RWS Group
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolWilliam Tambellini is a Senior Team Manager based in Los Angeles with over a decade of experience building and leading AI-focused engineering teams across RWS, SDL, Mentor Graphics and game/AI studios. He holds master's degrees in Cognitive Science and Artificial Intelligence from Bordeaux and EPFL, and has progressed from hands-on developer work in game AI to architecting production NLP and translation systems. William combines people leadership with deep backend expertise—contributing to high-performance C++ projects like ArrayFire and Flashlight where he added mixed-precision support, kernel tracing, and memory/cross-platform stability improvements. At RWS he moved from AI Software Architect to Dev Manager and now leads developer teams delivering language AI products, bridging research and productization. Colleagues value his ability to translate cutting-edge ML techniques into robust engineering practices and shipping optimized, production-grade systems. An early career background in game and semiconductor tooling gives him a pragmatic systems-first perspective that informs scalable ML deployments.
10 years of coding experience
20 years of employment as a software developer
Master's degree, Cognitive Science, Master's degree, Cognitive Science at Université Victor Segalen Bordeaux 2Master's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at EPFL (École polytechnique fédérale de Lausanne)
English, French
Github Skills (15)
cuda10
c-language10
cmake10
cprogramming-language10
gpu10
c119
machine-learning9
cpp9
deep-learning9
c179
memory-management9
opencl8
neural-network8
autograd8
performance-optimization8
Programming languages (18)
Github contributions (5)
flashlight/flashlight
Apr 2020 - Dec 2020
A C++ standalone library for machine learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:54 reviews, 9 commits, 9 PRs in 7 months
Contributions summary:William's contributions focused on enhancing the `flashlight` library by adding functionality and improving its core features. They implemented an option for static linking with cuDNN, enabling users to control the linking behavior. The user also addressed memory management by introducing caching mechanisms to prevent out-of-memory errors and improve performance. Additionally, they resolved MSVC compilation failures, improving the cross-platform compatibility of the library.
cppdeep-learningstandalone-libraryc-plus-plusmachine-learning
arrayfire/arrayfire
Jan 2019 - Jan 2020
ArrayFire: a general purpose GPU library.
Role in this project:Backend Engineer
Backend EngineerContributions:2 reviews, 15 commits, 18 PRs in 1 year
Contributions summary:William primarily focused on improving the ArrayFire GPU library by introducing new features and optimizing existing ones. They added an environment variable to trace and save generated kernels, enhancing debugging capabilities. The commits also included adding support for half-precision floating-point (f16) data type in matrix multiplications and other operations, expanding the library's capabilities for mixed-precision computing. Furthermore, the user addressed driver version checks and benchmark examples.
cudaarrayfirecppgpuscientific-computing
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