Research Scientist

About the Institute of Foundation Models We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.   As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.    
The Role   We are the AllWorld Team under the Institute of Foundation Model (IFM) at MBZUAI. At AllWorld, we are pioneering the development of the PAN (Physical, Agentic, and Networked) world models—the next-generation foundation models to unlock machine intelligence beyond lingual.    Our mission is to tackle the fundamental challenges of world modeling and establish a new paradigm for next-generation machine reasoning. We are looking for passionate individuals who share our vision and are eager to push the boundaries of AI together. 

Key Responsibilities

• Develop the foundational world model to accurately simulate the physical world.
• Collaborate with engineering and data teams to tackle key challenges in training the world model on large-scale clusters.
• Develop metrics and evaluation benchmarks to better assess model performance.
• Design and implement a scalable and efficient data annotation pipeline to ensure high-quality labeled data for training and evaluation.
• Optimize inference efficiency to enable real-time interaction. 

Areas of Focus

• Scalable Training Systems: Develop and optimize infrastructure for training multimodal LLMs and video diffusion models at massive scale. 
• Efficient Data Pipelines: Build scalable video data pipelines and annotation frameworks to support high-quality training data. 
• Inference Optimization: Enhance inference efficiency through optimization and distillation techniques to enable real-time interaction. 
• Visual Tokenization: Develop methods for discretizing visual features into tokens for improved model representation.