Ifm Us9mo ago

Research Scientist - World Modeling

Abu DhabiFull-timemid

Data ScienceData ScientistResearch ScientistDataData & AI

2 views0 saves0 applied

Apply Now

Quick Summary

Overview

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,

Technical Tools

Data ScienceData ScientistResearch ScientistDataData & AI

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

As a Research Scientist with the World Model Team, you'll help drive the development of PAN (Physical, Agentic, and Networked) world models — next-generation foundation models designed to push machine intelligence beyond language and into the realm of embodied, contextual reasoning. You'll tackle core technical challenges in world modeling and collaborate closely with a multidisciplinary team of researchers and engineers. We are looking for passionate individuals who share our vision and are eager to push the boundaries of AI together.

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, controllability, 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, MovieGen, 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.