Описание

Mobileye’s Visual GenAI team develops image and video generation capabilities for autonomous driving, with a growing focus on video and world models. They are looking for a hands-on researcher to help shape their research direction and turn ideas into working capabilities, with access to large proprietary driving datasets and close collaboration with the teams developing Mobileye’s next generation of autonomous driving technology.

Responsibilities

Research and develop image and video generation models, with a focus on temporal consistency, multiview coherence, and controllable driving-scene generation
Explore and adapt state-of-the-art world models for autonomous driving, helping shape the team’s research and technical direction
Own the full development lifecycle, from research and experimentation to model training, evaluation, and integration into real-world workflows
Build scalable, clean Python and PyTorch implementations for training and evaluating generative models
Collaborate with AI researchers and perception teams to develop generative capabilities for training and evaluating autonomous driving systems

Requirements

M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field
3+ years of industry experience in Deep Learning and Computer Vision, preferably working on generative AI or foundation models
A hands-on approach and the ability to turn research ideas into working solutions

Nice to have

Hands-on experience training image or video generation models, including diffusion and flow-based models
Experience with world models, multiview generation, 3D scene representations, or temporal modeling
Experience training or adapting foundation models, including Vision-Language Models (VLMs) and other multimodal models
Experience with distributed training and large-scale datasets
Publications in top-tier conferences such as CVPR, NeurIPS, ICCV, or ECCV

We offer

Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles

Hiring process

AI tools may be used to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information