Reinforcement Learning Researcher

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

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!