Overview
Research and develop reinforcement-learning planning algorithms, including policy architectures, reward design, training objectives, and optimization methods. Train and evaluate RL policies for difficult, interactive driving scenarios, building on the existing learning-based planner and complementary classical components. Develop evaluation methods and relevant metrics for safety, progress, comfort, and interaction quality, and use them to guide experiments and analyze failures. Build simulation-based training and closed-loop evaluation workflows. Turn research ideas into reliable components of the driving stack.
Full job description
What Will Your Job Look Like?
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Research and develop reinforcement-learning planning algorithms, including policy architectures, reward design, training objectives, and optimization methods.
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Train and evaluate RL policies for difficult, interactive driving scenarios, building on the existing learning-based planner and complementary classical components.
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Develop evaluation methods and relevant metrics for safety, progress, comfort, and interaction quality, and use them to guide experiments and analyze failures.
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Build simulation-based training and closed-loop evaluation workflows.
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Turn research ideas into reliable components of the driving stack.
All You Need Is:
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M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field.
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3+ years of hands-on industry experience in deep learning, including designing and training neural networks.
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Hands-on reinforcement-learning experience through research or practical application.
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Experience in autonomous driving, robotics, motion planning, simulation, or closed-loop evaluation- an advantage
Additional information
Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!
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