Machine Learning Engineer designing and implementing innovative ML models for autonomous vehicles at Woven by Toyota. Influencing the future of mobility through cutting-edge technology and human-centric innovation.
Responsibilities
Design and develop advanced machine learning models in the behavior space, specifically tailored for autonomous vehicles, utilizing 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 significantly 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.
Be a champion of 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.
Requirements
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.
Comfortable writing C++ code for integration with our autonomous vehicle platform.
3+ years of experience with deep learning approaches, such as supervised/unsupervised learning, transfer learning, multi-task learning, and deep reinforcement learning.
Extensive experience with learning-based planning approaches, including imitation learning, reinforcement learning, and state-of-the-art techniques for sequential modeling, such as Transformer architectures.
3+ years of experience covering machine learning workflows, data sampling and curation, preprocessing, model training, ablation studies, evaluation, deployment, and inference optimization.
Passion for self-driving car technology and its potential to impact humanity.
Strong communication skills with the ability to articulate concepts clearly and precisely.
Benefits
Excellent health, wellness, dental and vision coverage
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