Research Scientist - World Modeling

About the Institute of Foundation Models The Institute of Foundation Models (IFM) at MBZUAI is a research lab dedicated to meaningful foundation model research — building models from scratch, understanding them deeply, and publishing work that shapes the field. You’ll work alongside world-class researchers and engineers on problems that directly define the models we ship. The Role Join the PAN world model project — our effort to build world models: foundation models that simulate, predict, and interact with the physical world. As a Research Scientist, you’ll drive the core research behind PAN — large-scale video generation, interactive and action-conditioned world models, and their applications in robotics and embodied AI — and publish at top venues while turning breakthroughs into working systems.

Conduct original research on video world models, video diffusion models, and action-conditioned generation — from idea to publication and deployment. Design pre-training and post-training recipes for large-scale diffusion transformers, including scaling-law studies for video pre-training. Advance world action models / video action models and their applications in robotics and embodied agents. Develop rigorous evaluation benchmarks for physical accuracy, controllability, and interactivity. Collaborate with engineering and data teams on large-scale training, data curation, and simulation-based data generation.

PhD in Machine Learning, Computer Science, Computer Vision, Robotics, or a related field, with first-author publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, RSS, CoRL). Research experience with state-of-the-art video generative models and world models (e.g., Cosmos-3, LTX 2.3, Self-Forcing, Lingbot-World, or comparable systems). Deep expertise in at least one of the following areas: Full-stack data pipelines — large-scale video data pipelines and/or simulation data collection; annotation and filtering workflows for video / world model training. Model training & infrastructure — training large-scale diffusion transformers on large GPU clusters. Rendering engines & simulation — Unreal Engine and Blueprint-based gym environments, game-engine integration, building interactive simulated environments. World action models & robotics — world action models / video action models, action-conditioned video generation, world-model applications in robotics. Strong systems and engineering expertise in deep learning frameworks such as PyTorch. Highly proficient with modern AI coding agents and web-based coding tools (e.g., Claude Code, Codex, Cursor), and skilled at leveraging them to dramatically accelerate research workflows. Exceptional problem-solving skills and the ability to navigate ambiguity in rapidly evolving research areas.

Experience accelerating diffusion model inference (distillation, few-step generation, real-time interactive generation). Experience with visual tokenization and multimodal foundation models. Experience deploying world models in robotics or embodied-AI settings.

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