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

At Woven by Toyota, we work on a diverse set of problems ranging from solving optimization problems in 3D geometric computer vision, to minimizing latency on hardware accelerators, designing novel neural network architectures and applying and advancing the state-of-the-art of machine learning (ML) for perception, prediction, and motion planning. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with self-driving technology.

The Autolabeling team is looking for a Senior Machine Learning engineer to develop a state-of-the-art autolabeling system for autonomous driving. As a Machine Learning engineer in the Autolabeling team you will be responsible for designing and developing new Machine Learning models for our offboard automatic labeling systems, and deploying the models to label at scale the data necessary for the development of our next-generation autonomous vehicle platform and ultimately contribute to millions of Toyota production vehicles.

RESPONSIBILITIES:

• Perform Perception, Computer Vision, Graph or Large Language ML model R&D by prototyping, validating and iterating on existing and new model architectures.
• Own development of new ML models end-to-end from data strategy, initial development, optimization, production platform validation, and fine tuning based on metrics and on road performance.
• Influence the overall autolabeling stack architecture and technical direction.
• Coach and mentor junior members of the team to write high-quality code, provide high quality design and code reviews, and deliver rigorous reports from ML experiments.
• Work in a high-velocity environment and employ agile development practices
• Collaborate with stakeholders in downstream customer teams to define interfaces and requirements for the Autolabeling stack, and deliver high quality and high impact products to the customers

• MS, or higher degree, in CS/CE/EE, or equivalent in industry experience.
• Extensive experience with ML model development
• Experience with ML frameworks such as PyTorch, Tensorflow
• Experience in machine learning workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization
• Strong programming skills in Python or C
• Passionate about self driving car technology and its potential for humanity

$140,000- $260,000 a year

Your base salary is one part of your total compensation. At Woven by Toyota, we offer a base salary, a bonus, and a comprehensive benefits package. The California pay scale for this full time position is $140,000- $260,000. 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

By submitting your application you agree to the following terms: https://woven.toyota/en/applicant-privacy-notice

Our Commitment

・We are an equal opportunity employer and value diversity.

・We pledge that any information we receive from you will be used ONLY for the purpose of hiring assessment.