Reinforcement Learning Algorithm Researcher (CTO Team)

Mobileye is looking for an experienced Deep Learning Algorithm Researcher with a strong focus on Reinforcement Learning (RL) to join the CTO Group.

Mobileye’s CTO Group is a small, elite research and development group responsible for shaping the next generation of algorithmic solutions powering Mobileye’s autonomous driving systems, crowd-sourced HD maps, and advanced driver-assistance technologies.

The role focuses on building a learning-based planning system that has never been deployed at this scale before, leveraging Reinforcement Learning to address complex, interactive driving scenarios. You will work on policy-based decision making that provides a holistic, system-level view of autonomous behavior, while tackling a deeply challenging technological problem- taking ideas from research to real vehicles, iterating on algorithms, retraining models, validating results, and seeing the impact on the road within short development cycles.

This is a unique opportunity to take part in early-stage, high-impact projects, develop proof-of-concepts using cutting-edge technologies, and influence Mobileye’s long-term technological direction.

What will your job look like:

• Develop and optimize Reinforcement Learning-based planning algorithms, including network architectures, reward design, and training processes.
• Apply RL to complex, interactive driving scenarios, complementing classical planning where deterministic approaches fall short.
• Work with large-scale simulation environments to enable learning through interaction and diverse policy behaviors.
• Prototype algorithms in Python and migrate performance-critical components to efficient C++ / GPU-accelerated implementations.
• Take part in fast, iterative development cycles, collaborating closely with the CTO and the team to validate ideas and demonstrate working solutions.
  
  

• M.Sc. in Computer Science, Electrical Engineering, or a related field (Ph.D. - advantage).
• 3+ years of hands-on industry experience in deep learning, with a strong background in designing and training neural networks.
• Experience with Reinforcement Learning (academic coursework, research, or practical application) - strong advantage.
• Strong programming skills in Python.
• Experience with C++ and/or CUDA or other performance-oriented programming - advantage.
• Proven ability to tackle complex, open-ended problems and work effectively in iterative, research-driven environments.