About this role
What will your job look like?
- 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.
All you need is:
- M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related
field.
- 3+ years of hands-on industry experience in deep learning, including designing
and training neural networks.
- Hands-on reinforcement-learning experience through research or practical
application.
- Experience in autonomous driving, robotics, motion planning, simulation, or
closed-loop evaluation- an advantage
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!