Charles Bournhonesque
Quant Research Developer at Tower Research Capital
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolCharles Bournhonesque is a quant research developer and machine learning engineer with 11 years of experience building production ML pipelines for recommendation and scientific applications. He led Maps ranking and LLM integration at Snap, designed end-to-end data-to-serving systems, and previously drove graph-based inference for drug discovery using GNNs and tensor factorization. Comfortable in both research and systems engineering, he contributes to high-performance open-source Rust projects—helping improve the burn deep-learning framework and contributing performance and reflection fixes to the Bevy game engine. A Stanford M.S. in Computational and Mathematical Engineering and École Polytechnique background underpin his blend of mathematical rigor and practical optimization for low-latency, scalable ML systems.
11 years of coding experience
7 years of employment as a software developer
École PolytechniqueMathematics and Physics, Mathematics and Physics at Lycée Sainte-GenevièveMaster’s Degree Computational and Mathematical Engineering, Master’s Degree Computational and Mathematical Engineering at Stanford University
French, English, Korean, German
Github Skills (20)
autodiff10
game-engine10
game-dev10
machine-learning10
bevy10
deeplearning-ai10
deep-learning10
rust10
tensor10
game-development10
reflection9
wasm-pack9
onnx9
rust-wasm9
concurrency9
Programming languages (15)
Github contributions (5)
bevyengine/bevy
Jan 2023 - Apr 2025
A refreshingly simple data-driven game engine built in Rust
Role in this project:
Back-end Developer
Back-end DeveloperContributions:274 reviews, 68 PRs, 314 comments in 2 years 3 months
Contributions summary:Charles contributed to the Bevy game engine by fixing documentation comments and improving the codebase. They addressed issues related to UI alignment, and added system time availability in both native and wasm. Additionally, the user made the `EntityHashMap` and `PreHashMap` cloneable and added static assertions. They also added support for updating the tracing subscriber in LogPlugin and added support for the `TypeId` and `BinaryHeap` reflection implementations.
data-drivenbevygamedevrustgame-development
tracel-ai/burn
Nov 2022 - Aug 2024
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Role in this project:ML Engineer
ML EngineerContributions:6 reviews, 6 PRs, 11 comments in 1 year 9 months
Contributions summary:Charles primarily contributes to the development of the `burn` deep learning framework, focusing on enhancing its capabilities. Their work includes implementing new metrics like top-k accuracy and adding functionality for operations such as ONNX mean. The user also modifies and expands the existing JIT compilation kernel for performance, improving operations such as matmul and reduce. These additions and modifications indicate a focus on expanding the framework's feature set and optimizing its performance for deep learning tasks.
rustyburnndarraydeep-learningrust
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