Yang Song, Ph.D.

Architected and manage a cloud-native SaaS platform on AWS supporting ADAS data processing and data-closed loops for autonomous automotive systems. Oversaw compliance, data lifecycle, infrastructure reliability, and performance optimization for ADAS workloads across hybrid cloud architectures. Built Kubernetes-based data platforms that process 10M+ daily sensor data points using MSK, Lambda, S3, and Redshift, implementing automated ML workflows for ADAS model training. Drive business development initiatives with external automotive clients—gathering requirements, shaping technical proposals, and delivering POCs to win projects and expand partnerships.

Led radar processing algorithms for Motional's Robotaxi, enhancing sensor fusion libraries with Python, C++, and in-house cloud-based toolchains. Led POC development of lane change prediction and ADAS features for BMW and Stellantis clients. Delivered error-handling APIs with ROS, ensuring ASPICE-compliant solutions.

Built REST APIs in Java and Scala, enabling 6M+ daily customer notifications and GDPR compliance for EMEA. Migrated Hive to Spark, boosting performance 10x and cutting data pipeline runtime by 50%+. Designed and implemented scalable data processing architectures using Apache Spark, Kafka, and cloud-based data warehouses for real-time analytics and customer insights.

As Motion Planning Engineer at Auro Robotics during YC Summer 2015, I developed ROS-based path planning algorithms for a driverless campus shuttle from scratch. Worked directly with co-founders in Mountain View garage, integrating GPS and LiDAR for autonomous navigation. Successfully demonstrated at Stanford campus during YC Demo Day, securing $2.1M funding from Sam Altman and investors.