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Mobileye

Experienced Deep Learning Algorithm Developer

Ramat Gan, Israel

Location
Ramat Gan, Israel
Work
Hybrid
Employment
Full-time
Level
Senior
Function
Engineering
Pay
Not stated
Posted
2 months ago

About the role

The road algorithm team is responsible for the critical and challenging task of detecting the drivable lanes on the road. We focus on Deep Learning algorithms, classical Computer Vision and Geometric Modeling algorithms.

We are looking for an Algorithm Developer to help us advance our technology and push the boundaries of what's possible in autonomous driving.

What will your job look like

  • Research and develop state-of-the-art computer vision and deep learning algorithms for ADAS and autonomous driving.
  • Participate in the entire development cycle, from initial research and prototyping to deployment on real-time embedded systems within the vehicle.
  • Collaborate with engineering and research teams in Computer Vision, Machine Learning, and Data Engineering to solve real-life problems.
  • Analyze large datasets to improve algorithm performance and robustness.

All you need is

  • B.Sc. or M.Sc. in Computer Science or Engineering, with excellent academic performance.
  • 4+ years of hands-on experience developing deep learning algorithms, preferably in computer vision or perception for autonomous driving systems.
  • Solid understanding of data structures, algorithms, and deep learning principles.
  • Hands-on experience in algorithm development.
  • Strong problem-solving skills and the ability to work independently and as part of a team.

Nice to have

  • Experience with TensorFlow or PyTorch.
  • Familiarity with cloud platforms and tools (e.g., AWS, GCP).

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!

Source

Posted by Mobileye on their own hiring page, jobs.eu.lever.co. OptiTalent shows it as published, read 07 Nov 2026 01:33 UTC; the original is the reference.

View the original ad ↗

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