Machine Learning Engineer – 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 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:

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• 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.