Engineering Manager, Machine Learning Behavior Planning & Prediction

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.

TEAM The Behavior team at Woven by Toyota tackles Autonomy challenges for problems in prediction and trajectory planning. 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 architectures to advance state-of-the-art ML for Prediction and Motion Planning.   WHO ARE WE LOOKING FOR? We are seeking an experienced and technically strong, hands-on Engineering Manager to lead a group of highly talented and passionate engineers in the behavior team. This role is focused on developing and deploying state-of-the-art machine learning models for planning and prediction in AD/ADAS, along with their associated data processing pipelines and vehicle deployment. The ideal candidate has a deep passion for self-driving technology and its potential global impact. You will lead your team in advancing our core behavior machine learning models and accompanying pipelines. We are looking for an individual with domain expertise in machine learning, a comprehensive understanding of the model lifecycle, and experience developing tools and pipelines for behavior problems. Success in this role requires a candidate who thrives in a fast-paced environment, is hands-on, and is eager to push the boundaries of our technology. Prior experience building and deploying real-world autonomous driving products, collaborating with highly talented engineers, is essential.

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