Mobileye
Mobileye
Mobileye avtomobil sanoati uchun ilg‘or haydovchiga yordam berish tizimlari va avtonom boshqaruv texnologiyalarini ishlab chiqadi. Kompaniya haydovchiga yordam berishdan tortib avtonom boshqaruvgacha bo‘lgan turli qo‘llanmalarda transport vositalari xavfsizligini ta’minlash uchun kompyuter ko‘rishi, sun’iy intellekt, o‘ziga xos apparat vositalari va dasturiy ta’minotni birlashtiradi. Uning tizimlari 190 milliondan ortiq transport vositasida joriy etilgan bo‘lib, bu Mobileye’ning ko‘rishga asoslangan avtomobil texnologiyalarini keng ko‘lamli ishlab chiqarishga olib kirishdagi rolini ko‘rsatadi.

Applied AI Researcher, Visual GenAI at Mobileye (Hybrid, Israel)

Advance visual GenAI, diffusion models, and computer vision for Mobileye’s autonomous driving technology. Build synthetic-data solutions using proprietary datasets and large-scale computing infrastructure.

Tavsif

  • Lead the full development lifecycle, from training generative models to adapting state-of-the-art architectures for real-world autonomous driving applications.
  • Research, assess, and enhance GenAI and computer vision models through scalable, clean Python implementations.
  • Partner with AI researchers and perception teams to apply synthetic data in real-world AI systems.
  • Track emerging developments in artificial intelligence, generative modeling, and computer vision.
  • Create GenAI solutions with large proprietary datasets and high-scale computing infrastructure for autonomous vehicle applications.

Talablar

  • Master’s or doctoral degree in Computer Science, Electrical Engineering, or a related discipline.
  • At least three years of industry experience in deep learning and computer vision.
  • Advanced proficiency in Python and PyTorch.
  • Strong capabilities in problem solving, research, and analytical reasoning.
  • Practical experience with generative AI techniques such as diffusion models and GANs.
  • Experience with vision-language models and multimodal learning.
  • Publications at leading conferences such as CVPR, NeurIPS, or ECCV.
  • Hands-on experience with Linux, AWS, Docker, and virtual environments.
  • Knowledge of traditional computer vision methods.

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