Summary
Srinath Narayanan is a software engineer with 11 years of experience building production-scale ML systems, now working on ML and LLM-driven shopping ad relevance at Google. He previously led state-of-the-art fraud and risk modeling at JPMorgan Chase, where his distributed deep learning and PySpark pipelines processed terabytes of transactional data to detect millions in fraud monthly. A UC San Diego alumnus in Intelligent Systems, he blends classical signal and vision research with modern deep learning—having published on super-resolution and built CRNNs, Siamese signature embeddings, and YOLO-based extractors. Comfortable across research and production, he has shipped models into high-throughput environments and also led small teams and teaching roles, bringing a practical, multidisciplinary approach to sparse-data predictive problems. An engineer who started with DSP and optics experiments, he retains a hands-on penchant for low-level systems that informed his robust ML production designs.
11 years of coding experience
7 years of employment as a software developer
University of California, San Diego
B.E Electrical Electronics and Communications Engineering, B.E Electrical Electronics and Communications Engineering at Sri Sivasubramaniya Nadar College Of Engineering
AISSCE High School Mathematics and Computer Science, AISSCE High School Mathematics and Computer Science at P.S.Senior.Secondary.School
English, Tamil, Hindi