Sanjeet Suhag
Senior Autopilot Software Engineer at Tesla
Philadelphia, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolSanjeet Suhag is a Senior Autopilot Software Engineer with 11 years of experience building high-performance perception and tooling systems, currently driving autonomy work at Tesla. His background spans 3D graphics and simulation—having improved glTF asset pipelines at Cesium by implementing safe removal of unused textures and accessors—to applied AI tooling and production autopilot stacks. Comfortable across full-stack and systems-level code, he blends simulation, computer vision, and developer tooling to accelerate feature delivery in safety-critical domains. Based in Philadelphia, he pairs a Purdue Computer Science education with hands-on experience at research and defense organizations, bringing disciplined engineering and test-driven practice to complex pipelines. Notably, his open-source contributions improved stability and efficiency in a widely used glTF optimization project, reflecting attention to edge cases like mesh-instancing accessors.
11 years of coding experience
5 years of employment as a software developer
Mathematics and Computer Science, Mathematics and Computer Science at The Doon SchoolBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Purdue University
Hindi, French
Github Skills (7)
3d10
3ds10
javascript10
gltf10
convert10
converter10
testing10
Programming languages (9)
Github contributions (5)
CesiumGS/gltf-pipeline
Jan 2020 - Jul 2022
Content pipeline tools for optimizing glTF assets. :globe_with_meridians:
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:1 review, 14 commits, 4 PRs in 2 years 5 months
Contributions summary:Sanjeet focused on improving the glTF content pipeline tools by adding functionality to remove unused elements within the models. They implemented code to identify and remove unused textures, samplers, and images, optimizing the glTF assets. The user also created and improved tests to ensure the correct removal and management of these elements, contributing to the stability and efficiency of the pipeline. Additionally, the commits include exclusion of specific accessors, such as those related to mesh instancing, preventing unintended removal.
globepipelinewebgl2playcanvas3d-converter
songhongli1980/3d-tiles
Apr 2020 - Jul 2020
Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:
Contributions:68 commits in 2 months
geospatialheterogeneousspecificationearthstreaming
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