Woven by Toyota
Senior Machine Learning Engineer, Modeling & Pipelines, Behavior Planning
Woven by Toyota, Palo Alto, California, United States, 94306
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Senior Machine Learning Engineer, Modeling & Pipelines, Behavior Planning
role at
Woven by Toyota
We are 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, Arene, Woven City, and Cloud & AI, each powering our collaborative foundation toward a world with zero accidents and enhanced well‑being for all.
The Behavior team tackles autonomy challenges in prediction and trajectory planning. We analyze petabytes of multimodal driving data, solve optimization problems, minimize latency on hardware accelerators, deploy scalable ML training and evaluation pipelines, and design novel neural network architectures for state-of-the-art prediction and motion planning.
Who Are We Looking For? We seek a skilled Machine Learning Engineer to support the entire lifecycle of machine learning models, including performance tuning, model development, and infrastructure. Responsibilities include creating data pipelines, contributing to metrics pipelines, and integrating models into on‑vehicle software. This role involves designing and implementing innovative ML models that will impact millions of Toyota production vehicles.
Responsibilities
Design, build, and maintain ML pipelines that produce training and evaluation data for behavior and prediction models, including ETL, feature extraction, augmentation, and labeling workflows.
Implement auxiliary pipelines for model validation, metric computation, and on‑road performance integration.
Design and develop advanced machine learning models in the behavior space, tailored for autonomous vehicles, using deep learning 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.
Oversee the development of new ML models end‑to‑end, from data strategy and initial development to optimization, production platform validation, and fine‑tuning based on metrics and on‑road performance.
Lead large, multi‑person projects and influence the overall motion planning architecture and technical direction.
Enable and support other engineers by coaching, leading by example, and providing high‑quality code and design document reviews, as well as delivering rigorous reports from ML experiments.
Contribute significantly to the development of essential components for end‑to‑end ML training and deployment, from data strategy to optimization and validation.
Champion the scientific method and critical thinking to invent 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, Simulation, Infrastructure, and Tooling to drive unified solutions.
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, major deep learning frameworks, and software engineering best practices.
Built robust data pipelines, ETL workflows, and integration systems to support behavior prediction models.
Expertise with deep learning approaches, such as supervised/unsupervised learning, transfer learning, multi‑task learning, and deep reinforcement learning.
Good understanding of learning‑based planning approaches, including imitation learning, reinforcement learning, and state‑of‑the‑art sequential modeling techniques such as Transformer architectures.
Practical experience with ML training and evaluation workflows, including data sampling, preprocessing, and reproducible experiment management.
3+ years of experience covering machine learning workflows, data sampling and curation, preprocessing, model training, ablation studies, evaluation, deployment, and inference optimization.
Comfortable writing C++ code for integration with our autonomous vehicle platform.
Passion for self‑driving car technology and its potential to impact humanity.
Strong communication skills with the ability to articulate concepts clearly and precisely.
Nice to Have
Published research at top‑tier conferences (NeurIPS, RSS, IROS, ICRA, etc.).
Proven track record of deploying ML models at scale in self‑driving or related fields.
Familiarity with production‑level coding in time‑limited task schedules.
Experience with robot motion planning (e.g., trajectory optimization, sampling‑based planning, model predictive control).
Experience working with temporal data and sequential modeling.
Expertise in self‑driving challenges (Perception, Prediction, Planning, Simulation).
Salary For positions based in Palo Alto, CA, the base pay ranges from $161,000 to $264,500 a year, complemented by short‑term and long‑term incentives and a comprehensive benefits package.
What We Offer
Excellent health, wellness, dental and vision coverage.
A rewarding 401(k) program.
Flexible vacation policy.
Family planning and care benefits.
Our Commitment We are an equal‑opportunity employer and value diversity. Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.
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Senior Machine Learning Engineer, Modeling & Pipelines, Behavior Planning
role at
Woven by Toyota
We are 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, Arene, Woven City, and Cloud & AI, each powering our collaborative foundation toward a world with zero accidents and enhanced well‑being for all.
The Behavior team tackles autonomy challenges in prediction and trajectory planning. We analyze petabytes of multimodal driving data, solve optimization problems, minimize latency on hardware accelerators, deploy scalable ML training and evaluation pipelines, and design novel neural network architectures for state-of-the-art prediction and motion planning.
Who Are We Looking For? We seek a skilled Machine Learning Engineer to support the entire lifecycle of machine learning models, including performance tuning, model development, and infrastructure. Responsibilities include creating data pipelines, contributing to metrics pipelines, and integrating models into on‑vehicle software. This role involves designing and implementing innovative ML models that will impact millions of Toyota production vehicles.
Responsibilities
Design, build, and maintain ML pipelines that produce training and evaluation data for behavior and prediction models, including ETL, feature extraction, augmentation, and labeling workflows.
Implement auxiliary pipelines for model validation, metric computation, and on‑road performance integration.
Design and develop advanced machine learning models in the behavior space, tailored for autonomous vehicles, using deep learning 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.
Oversee the development of new ML models end‑to‑end, from data strategy and initial development to optimization, production platform validation, and fine‑tuning based on metrics and on‑road performance.
Lead large, multi‑person projects and influence the overall motion planning architecture and technical direction.
Enable and support other engineers by coaching, leading by example, and providing high‑quality code and design document reviews, as well as delivering rigorous reports from ML experiments.
Contribute significantly to the development of essential components for end‑to‑end ML training and deployment, from data strategy to optimization and validation.
Champion the scientific method and critical thinking to invent 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, Simulation, Infrastructure, and Tooling to drive unified solutions.
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, major deep learning frameworks, and software engineering best practices.
Built robust data pipelines, ETL workflows, and integration systems to support behavior prediction models.
Expertise with deep learning approaches, such as supervised/unsupervised learning, transfer learning, multi‑task learning, and deep reinforcement learning.
Good understanding of learning‑based planning approaches, including imitation learning, reinforcement learning, and state‑of‑the‑art sequential modeling techniques such as Transformer architectures.
Practical experience with ML training and evaluation workflows, including data sampling, preprocessing, and reproducible experiment management.
3+ years of experience covering machine learning workflows, data sampling and curation, preprocessing, model training, ablation studies, evaluation, deployment, and inference optimization.
Comfortable writing C++ code for integration with our autonomous vehicle platform.
Passion for self‑driving car technology and its potential to impact humanity.
Strong communication skills with the ability to articulate concepts clearly and precisely.
Nice to Have
Published research at top‑tier conferences (NeurIPS, RSS, IROS, ICRA, etc.).
Proven track record of deploying ML models at scale in self‑driving or related fields.
Familiarity with production‑level coding in time‑limited task schedules.
Experience with robot motion planning (e.g., trajectory optimization, sampling‑based planning, model predictive control).
Experience working with temporal data and sequential modeling.
Expertise in self‑driving challenges (Perception, Prediction, Planning, Simulation).
Salary For positions based in Palo Alto, CA, the base pay ranges from $161,000 to $264,500 a year, complemented by short‑term and long‑term incentives and a comprehensive benefits package.
What We Offer
Excellent health, wellness, dental and vision coverage.
A rewarding 401(k) program.
Flexible vacation policy.
Family planning and care benefits.
Our Commitment We are an equal‑opportunity employer and value diversity. Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.
#J-18808-Ljbffr