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Mobileye
Mobileye
1,001 – 5,000 Employees
H1B Visa SponsorArtificial IntelligenceAutomotiveManufacturing
Mobileye develops advanced driver-assistance systems and autonomous driving technology for the automotive industry. The company combines computer vision, artificial intelligence, proprietary hardware, and software to support vehicle safety across applications ranging from driver assistance to autonomous operation. Its systems have been deployed in more than 190 million vehicles, reflecting Mobileye’s role in bringing vision-based automotive technologies to large-scale production.
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Senior Deep Learning Researcher, Autonomous Vehicles

Research and develop deep learning systems for Mobileye’s autonomous vehicles, including large-scale neural networks built for its EyeQ chip. Improve model performance and bring research into production.

ILIsraelHybridFull-timeSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer
AWSCloudDockerLinuxPythonPyTorchTensorflow

Description

  • Develop and train deep learning models optimized for Mobileye’s custom EyeQ chip.
  • Create large-scale, multi-task neural networks for Mobileye’s autonomous driving system.
  • Solve end-to-end deep learning problems and deploy solutions in real-world systems.
  • Design new model architectures and use advanced training methods.
  • Optimize model performance within strict constraints.
  • Partner with software and hardware teams to turn research into production systems.
  • Help shape the core neural network architecture used in Mobileye’s flagship autonomous vehicle products.

Requirements

  • PhD in Computer Science or a related field; exceptional candidates with an MSc may be considered.
  • At least 4 years of hands-on experience developing deep learning algorithms in Python.
  • Experience building end-to-end deep learning pipelines covering data preparation, training, evaluation, and deployment.
  • Proficiency with at least one deep learning framework, such as TensorFlow or PyTorch.
  • Strong problem-solving skills and a research-focused approach.
  • Industry experience in deep learning or software development is an advantage.
  • Familiarity with hardware-aware model optimization is an advantage.
  • Experience with cloud platforms such as AWS, Docker, and Linux environments is an advantage.
  • Publications or contributions in deep learning, neural architecture search, knowledge distillation, or multi-task learning are an advantage.

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