Physical-AI – Applied Research (CTO Team)

Mobileye’s CTO Group is looking for an outstanding Physical-AI Applied Researcher to join our team.

The CTO Group is a small, elite research unit shaping the next generation of algorithmic foundations behind Mobileye’s autonomous driving systems. The group operates at the core of the decision-making and planning stack, addressing some of the most challenging problems in real-world autonomy.

We are seeking a researcher who thrives at the intersection of machine learning, decision-making, and algorithmic rigor - someone who is excited about advancing learning-based approaches for safety-critical, large-scale physical systems.

In this role, you will develop novel approaches for planning and decision-making in interactive, multi-agent driving environments. You will combine deep & reinforcement learning with classical algorithmic structure and formal reasoning. The problems are open-ended, scientifically challenging, and deployed at unprecedented scale.

This is a rare opportunity to conduct high-impact applied research, taking ideas from theory and papers into real-world autonomous systems at scale. If you’re excited about pushing the boundaries of learning-based decision-making, we’d love you to join us and help shape the future of Physical AI at Mobileye.

What will your job look like:

• Design and develop novel learning-based algorithms for decision-making and planning in complex physical environments.
• Advance model architectures for long-horizon reasoning, multi-agent interaction, and uncertainty-aware prediction.
• Integrate deep learning components into structured planning pipelines with clear formal objectives and safety constraints.
• Formulate problems mathematically and derive principled learning objectives grounded in real-world system requirements.
• Lead research directions from conception to full-scale production.
• Develop using Python (PyTorch or similar frameworks) as well as C++/GPU/Cuda.
  
  

• M.Sc/Ph.D. in Computer Science, Electrical Engineering, Robotics, Machine Learning, Applied Mathematics, or a related field.
• Proven experience in machine learning and deep learning.
• Demonstrated ability to conduct independent research (publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, etc. - advantage).
• Strong programming skills in Python; solid C++ experience - advantage.
• Experience in training large-scale models and working with real-world data.
• Intellectual curiosity, scientific ownership, and comfort operating in open-ended research environments.