Staff ML Engineer, Autonomy Vision Foundation Models

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. THE TEAM At Woven by Toyota, we work on some of the most challenging problems in autonomy — from 3D geometric computer vision and perception to prediction, motion planning, and safe deployment of ML systems in real vehicles. Our AD/ADAS organization develops production‑grade autonomy and active safety technologies that operate in complex, uncertain real‑world environments and ship at global scale. You’ll collaborate daily with ML researchers, software engineers, robotics engineers, and hardware teams to design, build, and deploy perception and world‑understanding systems that directly influence how vehicles see, reason about, and interact with the world.   WHO ARE WE LOOKING FOR? We are seeking a Senior / Staff Machine Learning Engineer to help lead the development and deployment of vision foundation models and large‑scale perception systems for our autonomy stack. This is a high‑impact individual contributor role for an engineer who combines strong ML modeling skills with system‑level thinking and a pragmatic approach to scale. You will own critical pieces of the perception ML lifecycle from data and training paradigms to validation and deployment and help evolve our infrastructure and processes as our ambitions and scale grow.  You’ll be expected to make thoughtful technical trade‑offs, design for efficiency, and drive progress under real‑world infrastructure, safety, and production constraints.