The CTO Group is a small, elite team focused on the decision-making module of Mobileye's autonomous driving system.
Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning.
The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data.
The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction.
What will your job look like:
- Research and develop innovative algorithms for autonomous driving planning and decision-making.
- Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems.
- Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies.
- Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance.
- Design and implement high-quality production-ready code in a research-driven environment.
- Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning.
All you need is:
- 5+ years of experience in algorithm development, machine learning, or AI research.
- Outstanding MSc in Computer Science, Computer Engineering, Mathematics, Physics, or a related quantitative field.
- PhD - an advantage.
- Strong programming skills in C++ and Python.
- Outstanding mathematical and analytical abilities.
- Passion for solving challenging research problems and the ability to quickly learn from scientific literature.