Develops deep learning models using imitation and reinforcement learning to generate safe, efficient driving trajectories for autonomous vehicles. Collaborates with Perception, Planning, and Simulation teams; requires ML expertise, Python fluency, and transformer experience.
277k – 407k
HybridML Engineering
About the role
Responsibilities
Develop new deep learning models using imitation learning and reinforcement learning to generate driving plans for autonomous vehicles.
Work on techniques to estimate the quality of driving plans along dimensions of safety, progress, comfort, etc.
Leverage large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field.
Develop metrics and tools to analyze errors and understand improvements of systems.
Collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments.
Qualifications
BS, MS, or PhD degree in computer science or related field.
Experience with training and deploying transformer-based model architectures and reinforcement learning.
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines.
Fluency in Python with a basic understanding of C++.
Extensive experience with programming, algorithm design, and strong mathematics skills.
Bonus Qualifications
Conference or Journal publications in Machine Learning or Robotics related venues.
Prior experience with Prediction and/or autonomous vehicles or robotics in general.
Leads development of ML algorithms for robot perception, including scene understanding, tracking, segmentation, and multi-modal foundation models using sensor data. Requires deep expertise in deep learning, computer vision, and production ML pipelines, collaborating across autonomy teams.
277k – 389k
HybridML Engineering
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