At Woven by Toyota, we’re bringing people, mobility technologies and infrastructure together to shape the future of movement.
About the role
Build, maintain, optimize and support large scale, cloud-native data ingestion, data storage, data processing and data serving systems. Develop and support large production real-time systems written and maintained in Python and C++. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Bachelor's degree in Computer Science, a related field, or equivalent practical experience
- 3+ years of experience with data structures/algorithms and professional software engineering in one or more programming languages (e.g., Python, Go, Java)
- Development experience with autonomous driving and/or data collection using C++ under embedded environments
- 2+ years designing and building data-intensive, concurrent, scalable applications
- Experience with cloud-based (e.g. AWS, GCP) microservice architecture, event-driven, distributed architectures
- Experience writing testable, modular code in Python
More about this role
Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.
Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.
Our Cloud and Data Engineering organization is working on accelerating autonomous driving by providing access to petabytes of data collected from our large fleet of autonomous and non-autonomous vehicles. Efficient, fast and cost-effective access to data at large scale is key to tackle the hardest problems in AD/ADAS, from developing the Machine Learning (ML) models for perception and prediction of human driving...
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