Charlie Sievers - Software Engineer at Boeing

Charlie Sievers

Software Engineer at Boeing

Renton, Washington, United States

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Summary

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Rockstar

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Top School

Charlie Sievers is a software engineer with seven years of experience building high-performance, research-driven systems for both national labs and aerospace. He developed parallel Python/C++ machine learning pipelines at Sandia National Laboratories to model interatomic interactions on hybrid supercomputing architectures and now focuses on OS development for embedded systems at Boeing. His open-source contributions to the widely used LAMMPS molecular dynamics project emphasize build systems, documentation, and physics-focused improvements in the USER-PHONON package, reflecting a rare blend of scientific rigor and engineering polish. Trained as a computational chemist (PhD, UC Davis) with a BS in chemistry, he translates domain science into production-ready code across distributed and resource-constrained environments. Colleagues value his ability to make complex tooling reproducible and usable, from compiler configurations to parallel algorithm implementations.

7 years of coding experience

Doctor of Philosophy - PhD, Computational Chemistry, Doctor of Philosophy - PhD, Computational Chemistry at University of California, Davis

California Polytechnic State University, San Luis Obispo

Github Skills (11)

simulations10

simulation10

c-language10

cmake10

molecular-dynamics-simulation10

cprogramming-language10

molecular-simulation10

makefile10

nonlinear-dynamics10

documentation9

Programming languages (6)

C++CSSSCSSJavaScriptHTMLPython

Github contributions (5)

lammps/lammps

Mar 2020 - Mar 2022

Public development project of the LAMMPS MD software package

Role in this project:

Back-end Developer

Contributions:4 reviews, 29 commits, 19 PRs in 2 years

Contributions summary:Charlie primarily contributed to the build system and documentation within the LAMMPS molecular dynamics software package. Their commits show modifications to build instructions (CMake and traditional make), including compiler settings and dependency information. The user also made changes to the code's documentation, demonstrating a focus on usability and clarity for users who build and use the software. The commits also include specific improvements to the dynamical matrix and third order tensor implementations within the USER-PHONON package.

lammpsmolecular-dynamicssimulationkokkos

charlessievers/FitSNAP

Jan 2020 - Apr 2022

Software for generating SNAP machine-learning interatomic potentials

Contributions:6 PRs, 212 pushes, 20 branches in 2 years 2 months

potentialsinteratomic-potentialsmachine-learningsnap

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