Summary
Rohin Garg is a Machine Learning Engineer based in New York with eight years of experience building ML systems for physical AI, robotics, and autonomous vehicles. He has combined research and product work—from improving GAN diversity at Berkeley AI Research and intent-aware recommendation models at Adobe to deploying trajectory-generation and multi-agent planning systems at Cruise and Scale AI. Rohin’s strengths are bridging theory and production: designing custom losses for uncertainty estimation, fine-tuning large models, and scaling autoregressive driving models on large datasets. His background in EECS (IIT Kanpur) and an MS in Computer Science and Robotics (UC San Diego) underpins a practical focus on real-time perception, trajectory prediction, and robust multi-agent behavior. Notably, he has repeatedly tackled catastrophic forgetting and OOD detection in generative and control settings, showing a pattern of solving subtle failure modes that emerge when models go from lab to road.
8 years of coding experience
4 years of employment as a software developer
University of California, San Diego
Indian Institute of Technology Kanpur
English, Hindi, Japanese