Research Scientist - Modeling

We are a dedicated research lab for building, understanding, using,and risk-managing foundation models. Our mandate is to advanceresearch, nurture the next generation of AI builders, and drivetransformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on thecore of cutting-edge foundation model training, alongsideworld-class researchers, data scientists, and engineers, tacklingthe most fundamental and impactful challenges in AI development. You will participate in the development ofgroundbreaking AI solutions that have the potential to reshapeentire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub forhigh-performance computing in deep learning, driving impactfuldiscoveries that inspire the next generation of AI pioneers.

We are the All World Team under the Institute of Foundation Model (IFM) at MBZUAI . At All World, we arepioneering the development of the PAN (Physical, Agentic, and Networked) world models —the next-generation foundation modelsto unlock machine intelligence beyond lingual.

Our mission is to tackle the fundamental challenges of world modeling and establish a new paradigm for next-generationmachine reasoning . We are looking for passionate individuals who share our vision and are eager to push the boundaries of AItogether.

• 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.

• 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.
• Quantitative Evaluation :
  Establish rigorous benchmarks to assess physical accuracy, cont rollability, and intelligence.
• Scaling Laws for Video Pretraining :
  Investigate scaling law principles to guide efficient video pre-training strategies.

• MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience.

• Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.
• Hands-on experience with state-of-the-art video generative models (e.g., Sora, Veo2, Movie Gen, CogVideoX, etc.).
• Experiences in building and optimizing large-scale video data pipelines.
• Experience in accelerating diffusion model inference for improved efficiency.
• Exceptional problem-solving and troubleshooting skills to tackle complex technical challenges.
• Strong systems and engineering expertise in deep learning frameworks such as PyTorch.
• Strong communication and collaboration skills for effective cross-functional teamwork.
• Ability to navigate ambiguity and drive projects in rapidly evolving research areas.
• Research contributions to top-tier conferences or journals (e.g., ICML, ICLR, NeurIPS, ACL, CVPR, COLM, etc.), with published work in relevant domains.

$150,000 - $450,000 a year

$150,000 – $450,000/year +…