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Location
Hybrid (Jerusalem, Israel)
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Sep 17, 2026. Mobileye scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Mobileye is an Israeli company founded in Jerusalem in 1999 by the computer scientist Amnon Shashua and the entrepreneur Ziv Aviram, built on the then-contrarian idea that a single camera and clever software could handle driver assistance without radar or lidar. Its EyeQ system-on-chip has shipped in more than two hundred million vehicles, making it the dominant supplier of vision-based driver assistance to the global car industry. Acquired by Intel for fifteen billion dollars in 2017 and returned to public markets in 2022, the company now sells escalating tiers from basic assistance through the SuperVision and Chauffeur hands-off systems up to the fully driverless Mobileye Drive platform.

Mobileye is looking for a Machine Learning Software Engineer, who will be challenged by bridging the gap between cutting-edge machine learning research and robust production deployment.

In this position, you will combine software engineering expertise with machine learning deployment knowledge, responsible for taking our algorithms and developing robust production solutions that serve them at scale.

The work at Mobileye’s algorithms department is fast-paced and requires staying ahead of the curve with the latest engineering solutions and best practices adopted across the ML community, while staying informed about emerging solutions in both computer vision and NLP domains and understanding the specific problems they address.

What will your job look like:

  • Your role will include developing production deployment systems for classical and machine learning algorithms from research and building robust, scalable inference pipelines.
  • You will develop primarily in Python and infrastructure tools (Kubernetes, Docker, etc.), taking part in both maintaining existing deployment systems and developing new production capabilities.
  • Finally, you will need to learn and implement new deployment technologies and best practices that can address emerging production challenges as they arise, while staying current with the latest MLOps and inference optimization techniques.

All you need is:

  • B.Sc. in Computer Science, Software Engineering, or related technical field.
  • Strong problem-solving skills and ability to tackle complex, real-world production challenges.

Nice to have:

  • Proficiency in Python and experience with containerization and orchestration technologies (Docker, Kubernetes).
  • Hands-on experience with model serving frameworks and inference optimization.
  • Background in distributed systems.
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