Summary
Siddhant Reddy is a software engineer with 10 years of experience focused on applied machine learning, computer vision, and embedded AI systems, currently building sim-to-real pipelines and low-latency vision deployments at Electric Sheep in San Francisco. His MS research at RIT explored deep unsupervised learning and a novel learned reconstruction loss to reduce reliance on annotated data, with practical fine-tuning for classification, segmentation, and RL. He has hands-on experience optimizing models for resource-constrained hardware—designing a glass-break detector that ran on a $5 device and shipping TensorRT-based deployments that cut inference latency by 50%. Siddhant also architected Nvidia Isaac Sim synthetic data tooling and used robot failure logs to improve robustness, bridging research and production for robotics perception. Curious and pragmatic, he pairs academic rigor with engineering discipline to turn ML inquiry about intelligence into tangible, cost-effective solutions.
10 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Computer Science, 3.76, Master of Science - MS, Computer Science, 3.76 at Rochester Institute of Technology
Don Bosco Institute Of Technology