Jerusalem
Full time
About us:
Mobileye is a global leader in Physical AI, developing computer vision, software, and hardware to power autonomous driving solutions and humanoid robots. Our mission is to bring autonomy into the real world by building intelligent technologies that are designed to expand accessibility and enhance everyday life, from advanced driver assistance systems (ADAS) and autonomous driving to robotics and humanoids.
Built on decades of automotive innovation and industrial-scale AI expertise, Mobileye technologies power more than 230 million vehicles worldwide. With teams around the world, we continue to innovate at the forefront of autonomous systems, advancing the next generation of Physical AI for the real world.
About the team:
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!
