Lawson Fulton
Autopilot Machine Learning Engineer at Tesla
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolLawson Fulton is an Autopilot Machine Learning Engineer in Palo Alto with 12 years of experience building production-grade AI, geometry processing, and simulation systems. He combines hands-on research (MSc in Graphics and Machine Learning) with founder experience at fulton.ai and technical leadership roles that shipped a cloud-based 3D generative-design platform and production Autopilot features at Tesla. Deep C++ and algorithmic roots—evidenced by contributions to the libigl geometry library—complement practical ML and cloud stacks in Python, C#, and TypeScript. Lawson excels at turning research prototypes into scalable products, having led multidisciplinary teams across computational geometry, optimization, and simulation. He brings an uncommon blend of academic publishing (Eurographics) and startup grit, routinely bridging numerical simulation insights with applied ML for real-world systems.
12 years of coding experience
8 years of employment as a software developer
Exchange Program, Exchange Program at Nanjing University of Aeronautics and AstronauticsMaster's degree, Computer Science — Graphics and Machine Learning, Master's degree, Computer Science — Graphics and Machine Learning at University of TorontoBMath, Computer Science, BMath, Computer Science at University of Waterloo
English
Stackoverflow
339reputation
11kreached
3answers
3questions
Github Skills (19)
algorithm10
algorithms10
c-language10
dijkstra10
html-template10
python-templates10
data-structure10
code-templates10
apptemplates10
geometry-processing10
data-structures10
cpp-templates10
cprogramming-language10
database6
bazel6
Programming languages (8)
Github contributions (5)
libigl/libigl
Sep 2018 - Jul 2019
Simple MPL-2.0-licensed C++ geometry processing library.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 7 PRs, 16 comments in 10 months
Contributions summary:Lawson contributed to the libigl library by adding a weights option to Dijkstra's algorithm implementation, enhancing its functionality for geometry processing applications. They also added template instantiations and performed const fixes to ensure the library's robustness. Further contributions included extending the library's capabilities with new features like polygon mesh to triangle mesh conversion and extending the igl::cat function. These changes suggest an active role in extending and improving the core functionalities of the library.
cppcluster-computingc-plus-plusgeometry-processingprocessing-library
lawsonfulton/lawsonfulton.github.io
Mar 2015 - Jul 2022
Contributions:49 pushes in 7 years 5 months
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