Machine Learning Engineer – World Modeling
Institute of Foundation Models Open · verified Oct 22, 2026 At a glance
Mid-level AI Research role at Institute of Foundation Models. Abu Dhabi · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Assemble video and simulation datasets for world-model training, handling capture, labeling, cleanup and selection
- Scale distributed training for diffusion-transformer and world-model workloads
- Build interactive game simulations that let teams train and assess world models
- Bring world-action or video-action systems into robotics
- Speed up live model responses and turn research demos into robust, repeatable software
What you bring
- A bachelor’s degree or higher in machine learning, computer science, robotics or a related area, or equivalent industry experience
- Practical experience with leading video-generation and world-model systems, such as Cosmos-3 or comparable models
- Deep skill in at least one listed area, including data pipelines, large-cluster training, simulation or robotics
- Strong PyTorch and deep-learning engineering skills, including diagnosing issues through training and inference
- Fluency with current AI-assisted coding and browser-based development tools, using them to accelerate engineering work
Who this fits
This role suits a mid-level engineer who wants to turn world-model research into working systems across data, training, simulation or robotics. You’ll work on-site in Abu Dhabi.
From the employer
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.
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.