Machine Learning Engineer – World Modeling

About the job

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 Machine Learning Engineer, you’ll own the engineering backbone of PAN: large-scale video and simulation data pipelines, distributed training for diffusion transformers, game-engine simulation environments, and world-model integration into robotics — turning research ideas into reliable, scalable systems.

What You'll Do

• Build and maintain large-scale video and simulation data pipelines — collection, cleaning, annotation, and filtering — to support world model training.
• Develop and optimize distributed training systems for large-scale diffusion transformers and world models.
• Build interactive simulation environments (e.g., Unreal Engine, Blueprint-based gyms, game integrations) for training and evaluating world models.
• Integrate world action models / video action models into robotics systems.
• Optimize inference and serving for real-time interaction, and turn research prototypes into reliable, reproducible systems.
• What We're Looking For
• BSc or above in Machine Learning, Computer Science, Robotics, or a related field, or equivalent industry experience.
• Hands-on 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 engineering expertise in deep learning frameworks such as PyTorch, with the ability to debug failures across the training/inference stack (memory issues, deadlocks, I/O bottlenecks).
• 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 engineering workflows.
• Nice to Have
• Experience accelerating diffusion model inference (distillation, few-step generation, real-time interactive generation).
• Practical experience with web scraping and crawling frameworks (e.g., scrapy, playwright, selenium) for web-scale data curation.
• Experience with Unreal Engine C++/Blueprint development or other game-engine programming.
• Experience deploying world models in robotics or embodied-AI settings.