Develop deep learning models using imitation and reinforcement learning for autonomous driving agents and planning. Requires advanced degree plus experience with RL, transformers, and production ML pipelines.
189k – 270k/yr
Hybrid5+ YOEML Engineering
About the role
Responsibilities
Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.
Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
Contribute to our 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 our systems.
Collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem.
Requirements
PhD degree in computer science or related field or master's degree and 5+ years of professional experience in a relevant field.
Experience in Planning and / or Prediction using Reinforcement Learning techniques.
Experience with training and deploying transformer-based model architectures.
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines.
Fluency in Python with a basic understanding of C++.
As a Software Engineer on the Behavior Capabilities team, you will develop and implement algorithmic advancements to expand the robot's driving abilities in complex scenarios, focusing on improving trip progress and vehicle uptime.
Develops deep learning models using imitation and reinforcement learning to generate safe, efficient trajectories for autonomous vehicles. Collaborates with perception and planning teams, leveraging large-scale ML infrastructure for production deployment in urban environments.
189k – 270k/yr
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