Researcher

• 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
• Help take the reinforcement-learning driving planner from simulation to real vehicles
• Define how driving behavior is evaluated, analyze failures in closed loop, and validate improvements on the road
• Work in a small team at Mobileye and collaborate with control and other algorithm teams

Researcher jobs in Israel

Reinforcement Learning Researcher – Learned-Policy Group