A market-leading Generative AI innovator pioneering next-generation synthetic media, generative video, and interactive conversational AI avatars.
Combining deep academic research with commercial success, the company is backed by tier-1 global venture capital funds and maintains strategic partnerships with world-renowned Fortune 500 tech and consumer enterprises.
Operating with an intimate, world-class algorithmic research group, the organization conducts groundbreaking research at the cutting edge of generative computer vision and machine learning.
The company is located in Tel Aviv within easy walking distance of the train station, operating on a hybrid model with two days working from home.
Role Description
- Serving as a Generative AI / Video Diffusion Researcher, joining a tight-knit, elite research team advancing the state of the art in generative media.
- Conducting deep algorithmic research, design, and experimentation focused on Video Diffusion Models and continuous video synthesis pipelines.
- Developing novel neural architectures for temporal consistency, high-fidelity motion generation, conditioning mechanisms, and video stylization.
- Training, fine-tuning, and evaluating massive-scale generative models leveraging distributed multi-GPU computing environments.
- Translating cutting-edge academic literature and proprietary concepts into robust, deployable production algorithms.
- Collaborating closely with Core Engineering and Product teams to transition novel generative capabilities into live consumer and enterprise products.
Requirements-
- Proven hands-on track record and specialized research experience with Video Diffusion Models – Mandatory
- Educational Background & Experience (one of the following paths) – Mandatory:
- Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field (with focus on Deep Learning / Computer Vision)
- M.Sc. in a relevant exact sciences/engineering domain + 3+ years of practical research experience
- B.Sc. in a relevant exact sciences/engineering domain + 6+ years of practical research experience
- Degree from a top-tier academic institution (Technion, Weizmann Institute, Tel Aviv University, Hebrew University, Ben-Gurion University, or Bar-Ilan University) – Mandatory
- Deep mathematical and practical understanding of deep learning frameworks (PyTorch), modern diffusion architectures, and temporal attention mechanisms – Mandatory
- Track record of publications in top-tier machine learning or computer vision conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, SIGGRAPH) – Advantage
- Experience with large-scale distributed training on cloud/cluster infrastructure – Advantage