Daniel Livingston
Software Engineer - Imaging at Anduril Industries
Westford, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolDaniel Livingston is a software engineer specializing in imaging with nine years of experience building high-performance GPU, low-level systems, and real-time software. He has driven impactful tooling and driver components at AMD—helping create Radeon GPU Detective and a cross-API profiling/tracing system adopted by AAA studios and awarded internally—and now applies that expertise to imaging at Anduril. His R&D work spans 3D polygonal meshing (FEM/CAD), geospatial/GIS analysis, and visualization, including leading open-source mesh and Voronoi tools used across U.S. DOE labs. Daniel combines systems-level languages (C/C++, Fortran, Rust) with higher-level prototyping (Python, Julia) to squeeze orders-of-magnitude performance gains from algorithms and parallel code. He pairs rigorous testing and CI/CD practices with practical deployment experience—having modernized legacy builds and ship internal package management—to make research-grade code robust and portable. Based in Westford, MA, he brings a rare mix of production GPU engineering and scientific computing that accelerates both tooling and simulation workflows.
10 years of coding experience
1 year of employment as a software developer
Master’s Degree Nanoscience, Master’s Degree Nanoscience at Arizona State UniversityMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Georgia Institute of TechnologyBachelor's Degree Physics, Bachelor's Degree Physics at New Mexico State University
English
Stackoverflow
1,228reputation
56kreached
29answers
29questions
Github Skills (200)
simulation-modeling10
mass10
python10
windows10
emoji10
heat10
docker-container10
hydrology10
precompiled10
scientific-computing10
modelica10
markdown10
javascript10
delaunay-triangulation10
linux10
Programming languages (19)
Github contributions (5)
daniellivingston/resume
Jul 2021 - Mar 2026
Contributions:53 pushes, 2 branches, 2 comments in 4 years 8 months
lanl/tinerator
Jan 2020 - May 2022
Intuitive and powerful unstructured geospatial mesh generation from GIS data.
Contributions:327 commits, 12 PRs, 96 pushes in 2 years 4 months
geospatialgismeshingmesh-generationgis-data
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