Staff Machine Learning Engineer, Motion Planning

Woven By Toyota
Palo Alto, CA

Woven by Toyota is the mobility technology subsidiary of Toyota Motor Corporation. Our mission is to deliver safe, intelligent, human-centered mobility for all. Through our Arene mobility software platform, safety-first automated driving technology and Toyota Woven City — our test course for advanced mobility — we’re bringing greater freedom, safety and happiness to people and society.

Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship. We leverage these complementary strengths to amplify the capabilities of drivers, foster happiness, and elevate well-being.

Team

Our Motion Planning and Prediction team is seeking a Staff Machine Learning Engineer to develop state-of-the-art ML models that drive the future of Toyota autonomous vehicles. As a key contributor to our motion planning system, you will focus on ML research and development, optimizing models for deployment on next-generation autonomous platforms—bringing cutting-edge technology to millions of vehicles.

Who are we looking for?

The ideal candidate has hands-on experience building and deploying ML models in real-world applications, a strong technical foundation in deep learning, and a passion for autonomous systems. We value diverse expertise, so even if your background doesn’t match every requirement, we encourage you to apply.

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Responsibilities
  • Develop machine learning models for autonomous motion planning using techniques such as imitation learning, deep learning, and large-scale data-driven modeling.
  • Prototype, iterate, and validate new ML architectures, optimizing performance and robustness.
  • Contribute to the full ML model lifecycle, from data strategy and initial development to optimization, validation, and real-world performance tuning.
  • Work closely with cross-functional teams (Perception, Simulation, Infrastructure) to ensure seamless integration of ML models into motion planning and prediction pipelines.
  • Write high-quality, production-ready code and collaborate in code reviews to uphold engineering standards.
  • Apply rigorous experimentation methodologies to evaluate models and improve real-world driving behaviors.

Qualifications
  • PhD or Masters in Machine Learning, Robotics, Computer Science, or related fields.
  • 8+ years of experience with ML frameworks (PyTorch, TensorFlow, etc.).
  • Strong understanding of learning-based planning, including imitation learning, deep learning, sequential modeling, and Transformer architectures.
  • Proficient in Python or C++ for ML development and deployment.
  • Hands-on experience with data processing, model training, and performance evaluation for deep learning pipelines.
  • Passionate about autonomous driving technology and its potential impact.

Nice to Have
  • Experience in robot motion planning techniques such as trajectory optimization, sampling-based planning, or model predictive control.
  • Background in self-driving technologies, including Perception, Prediction, Mapping, Localization, and Simulation.

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The base pay for this position ranges from $161,000- $264,500 a year.

Your base salary is one part of your total compensation. We offer a base salary, short term and long term incentives, and a comprehensive benefits package. The total compensation offered to an employee will be dependent upon the individual's skills, experience, qualifications, location, and level.

WHAT WE OFFER

We are committed to creating a modern work environment that supports our employees and their loved ones. We offer many options of the best programs to allow you to do your most meaningful work and to help you shape the future of mobility.

・Excellent health, wellness, dental and vision coverage

・A rewarding 401k 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.

Posted 2025-09-22

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