✦Role summaryAI-generated
You will build and maintain large-scale video and simulation data pipelines for the PAN world model project, ensuring efficient data ingestion and preprocessing. You will also design and deploy distributed training systems for diffusion transformers and integrate the resulting world models into robotics and game-engine simulation environments.
About this role
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.
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