„Mobileye“ kuria pažangias vairuotojo pagalbos sistemas ir autonominio vairavimo technologijas automobilių pramonei. Įmonė derina kompiuterinę regą, dirbtinį intelektą, patentuotą aparatinę įrangą ir programinę įrangą, kad užtikrintų transporto priemonių saugumą įvairiose srityse – nuo vairuotojo pagalbos iki autonominio vairavimo. Jos sistemos įdiegtos daugiau nei 190 milijonų transporto priemonių, o tai atspindi „Mobileye“ vaidmenį diegiant rega pagrįstas automobilių technologijas masinėje gamyboje.
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.
Aprašymas
- 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.
Reikalavimai
- 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.