Kevin Spiekermann
Applied Scientist II at Amazon
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolKevin Spiekermann is an applied scientist with eight years of experience applying traditional and deep machine learning to chemistry, generative modeling, and large language model agents. Currently at Amazon AGI, he builds LLM-driven GenAI systems for Alexa/AIDo and scales agentic solutions, after leading generative chemistry and uncertainty-aware active learning at Merck. His MIT PhD work produced message-passing neural networks and a high-quality kinetics dataset, plus practical software contributions to Reaction Mechanism Generator that improved robustness and parallelism. Kevin blends production engineering—automation and back-end reliability—with research rigor, demonstrated by CCSD(T)-level datasets and peer-reviewed scholarship. He enjoys cross-functional collaboration to translate models into business and scientific impact and often focuses on uncertainty quantification to prioritize real-world decisions. Based in Cambridge, MA, he pairs domain expertise in cheminformatics with hands-on open-source contributions to accelerate scientific workflows.
8 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Chemical Engineering, Doctor of Philosophy - PhD, Chemical Engineering at Massachusetts Institute of TechnologyUniversity of California San Diego
Github Skills (14)
kinetics10
chemistry10
python10
automation9
automations9
reaction8
algorithms8
data-structures8
algorithm8
testing8
react8
data-structure8
github7
documentation7
Programming languages (3)
Github contributions (5)
ReactionMechanismGenerator/RMG-Py
Oct 2019 - Dec 2022
Python version of the amazing Reaction Mechanism Generator (RMG).
Role in this project:
Back-end Developer & Automation Engineer
Back-end Developer & Automation EngineerContributions:62 reviews, 96 commits, 25 PRs in 3 years 2 months
Contributions summary:Kevin primarily focused on improving the functionality and usability of the Reaction Mechanism Generator (RMG) Python code. Their contributions include optimizing performance by reducing the frequency of print statements and implementing a command-line argument for specifying the number of processors. The user also made minor updates to the commented input file and addressed several bug fixes and code enhancements, such as normalizing mole fractions and ensuring proper file handling. Furthermore, they added the capability to save seed files at specified intervals, and improved the robustness of the code, likely for reliability.
mechanismchemical-engineeringchemistrypythonreaction
kspieks/chemprop
Jul 2021 - Oct 2024
Contributions:2 reviews, 1 PR, 27 pushes in 3 years 3 months
property-predictionpredictiondeep-learningneural-message-passingmolecule
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