Overview
About Woven by Toyota
Full job description
About Woven by Toyota 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.
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TEAM The Enterprise AI team is dedicated to empowering Toyota and its affiliates with a robust platform for AI innovation. Our mission is to provide a comprehensive, end-to-end machine learning ecosystem that propels the development of groundbreaking projects, such as autonomous driving, within the Toyota Group. As a standardized machine learning platform under Woven by Toyota, we aim to streamline every facet of AI development, from training and inference to MLOps, thereby enhancing the safety, convenience, and autonomy of Toyota vehicles.
Within this dynamic environment, the Data Platform Engineering team plays a pivotal role. We design and implement scalable, globally distributed data delivery solutions tailored for Toyota and its partners. Our team is at the forefront of developing both human-assisted and automated data labeling services, and we engage collaboratively across various model development and AI solution initiatives. Through these efforts, we ensure that data is not only accessible but also actionable, driving innovation and efficiency across the enterprise.
WHO ARE WE LOOKING FOR? As a Software Engineer, you will help develop the platform that enables creation and management of labeled datasets while working with seasoned engineers in various fields, such as Software Engineers, ML Engineers, Data Scientists, delivering and maintaining software for data distributed across different regions. Expect large datasets, shipping them globally. We aim to change data acquisition and delivery of human/machine-labeled data to expedite development of machine learning projects.
You will have both technical and communicational skills. As a part of the team, you are a believer in healthy, constructive, and optimistic feedback, as we encourage each other to improve our development practices; refactoring, rewriting legacy code, profiling, code style, and code reviews.
Design, implement, and deploy features from inception to completion Help solve complex problems, deliver state of the art solutions Collaborate with the team lead and software engineers to develop the backend of labeling suites, ensuring both functional and non-functional requirements of the product are met Enable support for multiple machine learning training data formats and facilitate on-the-fly conversions Integrate with various data sinks, including machine learning data visualization solutions, to manage datasets owned by the Data Annotations Engineering team Work closely with frontend developers to establish and maintain API contracts Report directly to the manager overseeing the Data Annotation Engineering team
A minimum of 4 years of experience in Python development, with at least 2 year dedicated to asynchronous Python programming, and a foundational understanding of machine learning Proficiency in working with large datasets, including databases with extensive rows or documents, and a solid grasp of concurrency, distributed computing, and blob storage Familiarity with event-driven architectures utilizing multiple message queues (channels) Knowledge of major RDBMS and NoSQL databases, such as PostgreSQL and MongoDB Hands-on experience with Kubernetes Ability to work in the office 3 days per week in accordance with our hybrid work model Proficiency in English at a business level
Contributions to open-source projects and the ability to analyze open-source software Familiarity with spatial/geometry information or experience with vector databases Experience with PyTorch data loaders and working with 2D/3D-based machine learning training data formats Understanding of machine learning, with a focus on deep learning Knowledge of image and point cloud processing techniques Proficiency in one or more programming languages commonly used in machine learning or massively parallel computing environments
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