Building infrastructure supporting software-defined vehicles (SDVs), autonomous driving development, and large-scale validation of virtual vehicles.
Arene AI Team – ML Infrastructure
•Designed and built cloud-native infrastructure for distributed training and large-scale data processing for AD/ADAS machine learning models.
•Developed a Distributed Processing Engine using Flyte Map Tasks, replacing Apache Spark workflows and reducing operational overhead by ~25% while improving observability.
•Platform adopted by 12+ autonomous driving ML teams for high-volume workloads including data annotation, ETL pipelines, and data-driven planning.
•Implemented Kubernetes-based (Go) orchestration for distributed PyTorch training, enabling a faster and more cost-efficient alternative to managed training services.
Arene Tools – SDV Validation Platform
•Led development of Vertex Studio, a cloud-native platform for test case authoring, execution, and results analysis for virtual vehicle validation.
•Scaled the system to support thousands of concurrent validation requests and improved performance by ~60% through optimized task orchestration and data handling.
•Designed as a cloud-native, multi-cloud solution to support large-scale simulation and testing for Toyota’s software-defined vehicle ecosystem.
•Contributing to next-generation validation infrastructure to enable earlier, large-scale testing of vehicle software prior to deployment.
•Named inventor on patent filings related to vehicle software validation, test environment design, and safety-focused system workflows.
