Mobileye
Mobileye
Mobileye ऑटोमोटिव उद्योग के लिए उन्नत ड्राइवर-सहायता प्रणालियाँ और स्वायत्त ड्राइविंग तकनीक विकसित करती है। कंपनी कंप्यूटर विज़न, कृत्रिम बुद्धिमत्ता, स्वामित्व वाले हार्डवेयर और सॉफ़्टवेयर को मिलाकर ड्राइवर सहायता से लेकर स्वायत्त संचालन तक के अनुप्रयोगों में वाहन सुरक्षा को बेहतर बनाने में मदद करती है। इसकी प्रणालियाँ 190 मिलियन से अधिक वाहनों में तैनात की जा चुकी हैं, जो विज़न-आधारित ऑटोमोटिव तकनीकों को बड़े पैमाने पर उत्पादन तक पहुँचाने में Mobileye की भूमिका को दर्शाता है।

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.

विवरण

  • 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.

आवश्यकताएँ

  • 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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