At Woven by Toyota, we’re bringing people, mobility technologies and infrastructure together to shape the future of movement.
About the role
Design, build, maintain, optimize and support the ML Platform’s systems and tools for perception, prediction, and planner development, allowing numerous ML engineers to effectively & efficiently iterate on dataset curation, ML modeling, training, evaluation and deployment of ML models into our functionally safe AD/ADAS stack, shipped in millions of Toyota vehicles.
What they're looking for
- BSc / BEng (MS / PhD nice-to-have) in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience
- 5+ years of experience with data structures, algorithms, design patterns, and software engineering best practices
- 2+ years of experience with UNIX-based systems (Linux or similar), Python, and PyTorch/Tensorflow
- Experience with Docker and CI systems such as GitHub Actions
- Business-level proficiency in English, able to write technical documents (e.g., for software documentation)
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.
At Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. Our work involves a variety of challenges, such as analyzing petabytes of multimodal driving data, solving optimization problems, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network...
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