Salil Kapur
AI Infrastructure Engineer at Reflex Robotics
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolSalil Kapur is an AI Infrastructure Engineer with 14 years of software engineering experience building data and compute platforms for machine learning at companies including Apple, Luma AI, and now Reflex Robotics. He has deep experience in data platform services and ML SDKs from his time at Apple and a track record of shipping production data infrastructure and diagnostics work at Microsoft. An active contributor to open-source computer vision tooling (notably enhancements to the SimpleCV framework), he blends systems-level engineering with practical CV and data-processing expertise. Based in New York, he holds a Master’s in Computer Science from NYU and is skilled at turning complex ML data needs into reliable, scalable infrastructure.
14 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering (BE) Computer Science, Bachelor of Engineering (BE) Computer Science at BITS Pilani K.K Birla Goa CampusMaster’s Degree Computer Science, Master’s Degree Computer Science at New York UniversityScience, Science at DPS Mathura Road,New DelhiDPS Bhopal,Bhopal
Hindi, English
Stackoverflow
59reputation
17kreached
5answers
2questions
Github Skills (12)
computer-vision10
opencv10
python10
image-processing10
numpy9
deep-learning6
kernel-module6
linux6
hough-transform6
visual-studio6
image6
scikit-image5
Programming languages (7)
Github contributions (5)
sightmachine/SimpleCV
Dec 2012 - Apr 2013
The Open Source Framework for Machine Vision
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:31 commits in 4 months
Contributions summary:Salil primarily contributed to the development of the `SimpleCV` framework, focusing on implementing and improving image processing functionalities. Their work included the addition of a `grid` method for image manipulation, along with a `findGridLines` method. Furthermore, they updated the `grid` method, implemented a `watershed` method, and refined existing functionalities related to line scans and Haar cascades, demonstrating a strong understanding of computer vision techniques. The user also introduced improvements to the ColorMap and added new grayscale conversion methods.
pytorchpythonvisiondeep-learningcomputer-vision
salilkapur/SimpleCV
Jan 2013 - Apr 2013
Contributions:30 commits in 3 months
pytorchvisiondeep-learningobject-detectioncomputer-vision
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