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.

 

\nWhat 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:\n

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!