Teams at Woven by Toyota

Build and maintain scalable data infrastructure for autonomous-driving applications. Responsibilities include developing vehicle-fleet data ingestion pipelines, distributed cloud systems, data processing and transformation services, workflow orchestration, and reliable data platforms. Collaborate with computer vision and machine learning engineers to support large-scale ML development, validation, and simulation. The role requires strong Python or object-oriented programming, distributed application development, cloud experience, RPC protocols, testing, observability, and data pipeline expertise.

Lead and grow a team of Data Scientists, Data Engineers, and ML Ops Engineers developing large-scale autonomy data pipelines and machine learning solutions for perception and planning. Define technical direction, data strategy, evaluation frameworks, and production processes. Guide data collection, curation, labeling, sampling, model deployment, and optimization while ensuring scalability, cost efficiency, safety, and quality. Collaborate globally across infrastructure, simulation, tooling, and autonomy teams.

Lead data strategy and engineering for AD/ADAS: collect, sample, label, and curate large-scale vehicle and simulator data; build evaluation frameworks and scalable pipelines; analyze data to improve perception and planning models; collaborate across teams and mentor colleagues to deploy production-ready ML solutions.