Reinforcement Learning Researcher | Learned-Policy Group

Build the intelligence behind the next driving decision.

We're building a reinforcement-learning driving planner for complex,

interactive road scenarios. We're looking for a researcher to help take it from

simulation to real vehicles.

You'll develop policy models, rewards, and training methods. You'll define how

driving behavior is evaluated, analyze failures in closed loop, and validate

improvements on the road. You'll work in a small team at Mobileye and

collaborate with control and other algorithm teams.

This is a high-impact role with direct influence on a core part of Mobileye's

driving technology and its future products.

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