Vijayaganesh M
Senior Machine Learning Engineer - Computer Vision at Motional
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolVijayaganesh M is a Senior Machine Learning Engineer specializing in computer vision with eight years of experience building perception systems for robotics and autonomous platforms in the San Francisco Bay Area. He has driven production-ready perception work across Motional, AppHarvest, Root AI, and Staples, blending deep robotics intuition from an MS in Robotics and Computational Intelligence with practical systems engineering. Comfortable across the stack, he has hands-on experience improving backend code quality in open-source projects and shipping vision models and sensors into deployed robots and vehicles. Known for bridging research and product, he focuses on reliable, maintainable perception pipelines that scale from prototype to fleet. A background in instrumentation and control gives him a systems-level perspective that helps diagnose hardware-software interactions beyond typical ML scope.
8 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Robotics and Computational Intelligence, Master of Science - MS, Robotics and Computational Intelligence at North Carolina State UniversityAnna University, Chennai
English, Tamil
Stackoverflow
1reputation
0reached
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Github Skills (6)
ruby-rails10
rails10
back-end-development10
ruby10
expert9
expert-system9
Programming languages (3)
Github contributions (5)
expertiza/expertiza
Mar 2019 - Mar 2019
Expertiza is a web application through which students can submit and peer-review learning objects (articles, code, web sites, etc). The Expertiza project is supported by the National Science Foundation.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits, 1 PR, 2 comments in 1 day
Contributions summary:Vijayaganesh's commits focused on addressing Code Climate issues within the Expertiza web application. These fixes involved modifications to multiple Ruby files, including models like `Scale`, `Question`, `Participant`, `Role`, `Rscore`, and `QuestionnaireNode`. The changes primarily involved code cleanup and improvements to adhere to code quality standards as indicated by the "Code Climate" tag. The user also modified controllers and views related to tree display and teams.
nationalpeer-reviewexpertiza-wikidjangoexpertiza
root-ai/depth-quality
Sep 2020 - Jan 2022
Measuring Depth Quality With 3D-Printed Fixtures
Contributions:4 reviews, 15 commits, 6 PRs in 1 year 4 months
measuringqualitydepthfixtures
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