Simon Kassel
Software Engineer, Project Lead at Element 84
Burlington, Vermont, United States
Summary
🤩
Rockstar🎓
Top SchoolSimon Kassel is a full-stack software engineer and project lead with nine years of experience building production-grade systems and data-driven solutions, currently leading projects at Element84. His background blends data science and geospatial expertise from roles at Azavea and academic work at UPenn, where he taught spatial analysis and advanced GIS topics. He contributes to open-source geospatial ML tooling—improving core modules and test coverage in the notable Raster Vision project—bringing a practical focus on code quality, devops, and handling complex geojson geometries. Comfortable moving between back-end services, ML workflows, and deployment, he’s known for refactoring legacy codebases into clearer, more maintainable systems. Based in Burlington, VT, he pairs rigorous academic training in city and regional planning with hands-on engineering to solve spatially complex problems end-to-end.
9 years of coding experience
9 years of employment as a software developer
High School, High School at Burlington High SchoolB.A., Political Science, Geography, B.A., Political Science, Geography at Macalester CollegeMaster’s Degree, City and Regional Planning, Master’s Degree, City and Regional Planning at University of Pennsylvania
Stackoverflow
61reputation
4kreached
0answers
1question
Github Skills (12)
computer-vision10
deep-learning10
geospatial10
python10
testing9
git9
object-detection9
pytorch9
machine-learning8
semantic-segmentation8
docker6
r6
Programming languages (11)
Github contributions (5)
azavea/raster-vision
May 2018 - Sep 2019
An open source library and framework for deep learning on satellite and aerial imagery.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:44 commits, 12 PRs, 46 pushes in 1 year 4 months
Contributions summary:Simon primarily worked on refactoring and improving the existing codebase, focusing on naming conventions and updates to core modules like `chain_workflow.py` and `ml_task.py`. They addressed issues related to handling multipolygons in geojson files, implementing tests and making necessary code changes. Additionally, the user merged remote branches and improved existing unit tests, indicating a focus on code quality and integration.
pytorchgeospatialsemantic-segmentationdeep-learningsatellite
azavea/building-inspection-prediction
Sep 2017 - Dec 2018
Machine Learning model to predict building code compliance repeat offenders
Contributions:23 commits, 2 PRs, 19 pushes in 1 year 3 months
compliancemachine-learningpredict
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.