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 Behavior team at Woven by Toyota tackles Autonomy challenges for problems in behavior 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 Behavior Planning. We are looking for driven and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications.
WHO ARE WE LOOKING FOR?
The Behavior team is looking for a skilled Machine Learning Engineer to help advance cutting-edge machine learning systems for behavior planning in autonomous driving. You will have the chance to design and implement innovative machine learning models for our next-generation autonomous vehicle platform, influencing millions of Toyota production vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity.
RESPONSIBILITIES
Lead the design and development of advanced machine learning models specifically tailored for autonomous vehicles utilizing deep learning, simulation, and large-scale data analysis
Deploy scalable and efficient ML models on our autonomous vehicle platform
Integrate modern technologies with rigorous safety standards while maintaining cost efficiency
Significantly contribute to development of needed components for end-to-end ML training and deployment, from data strategy to optimization and validation
Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions
Work in a high-velocity environment and employ agile development practices
Collaborate closely with teams such as Perception, Motion Planning, Simulation, Infrastructure, and Tooling to drive unified solutions
Work in a hybrid workspace, with the requirement to be present in our Nihonbashi (Japan) office three days per week
MINIMUM QUALIFICATIONS
MS or PhD in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience
3+years of experience with Python, any major deep learning framework, and software engineering best practices
Comfortable in writing C++code to help integrate with our autonomous vehicle platform
3+years of experience with deep learning approaches such as pretraining, distillation, unsupervised/supervised learning, multi-task learning, and/or deep reinforcement learning
3+years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization
Strong communication skills with the ability to communicate concepts clearly and precisely
NICE TO HAVES
Published research at top-tier conferences (NeurIPs, CVPR and similar)
Proven track record of deploying ML models at scale in self-driving or related fields
Experience developing large-scale ML models (e.g., LLMs, VLMs, VLAs)
Familiarity with production-level coding in time-limited task schedules
Experience with computer vision (e.g.multi-view geometry, camera calibration, depth estimation, neural radiance fields, gaussian splatting, simultaneous localization and mapping)
Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control)
Experience with temporal data and/or sequential modeling
Experience in self-driving challenges (Perception, Prediction, Mapping, Localization, Planning, Simulation)