Sanjay Srinivasan
Embedded Software Engineer at Collins Aerospace
Canada
Summary
👤
Senior🎓
Top SchoolSanjay Srinivasan is an embedded software engineer with nine years of experience building firmware and scalable software systems across aerospace, telematics, and data companies. Currently at Collins Aerospace, he brings a background that spans vehicle telematics at Motive, web crawling and bots at Numerator, and hands-on deep learning and data science internships at NXP and Capital One, reflecting a rare blend of embedded, backend, and ML-adjacent experience. Trained in Mechatronics, Robotics and Automation at the University of Waterloo, he combines hardware-aware thinking with software engineering discipline to deliver reliable, production-ready systems. Colleagues value his ability to bridge low-level embedded constraints with higher-level data and automation workflows, and his career shows a consistent pattern of moving between prototyping and productionization. Based in Canada, he often operates at the intersection of electronics, firmware, and data-driven software—a practical engineer who can take ideas from circuit to cloud.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Applied Science (BASc) Mechatronics Robotics and Automation Engineering, Bachelor of Applied Science (BASc) Mechatronics Robotics and Automation Engineering at University of Waterloo
Github Skills (27)
python10
machine-learning10
deep-learning10
tensorflow10
data-science9
pytorch9
neural-network9
pruning8
deep-neural-networks8
model-compression8
ml8
optimizers7
keras-neural-networks7
keras7
jax7
Programming languages (4)
Github contributions (5)
s36srini/trucking-network
Jun 2021 - Aug 2021
A graphical interface and backend that compares a simulation of a network of trucks, self driving and normal trucks, and only self driving trucks.
Contributions:3 reviews, 33 commits, 16 PRs in 2 months
graphical-interfaceself-drivingsimulationgraphicalbackend
s36srini/tf-keras-surgeon
Aug 2019 - Dec 2023
Pruning and other network surgery for trained TF.Keras models.
Contributions:1 push in 4 years 4 months
keras-modelsdeep-learningpruningtensorflowtf-keras
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