Research Scientist - World Modeling

About the Institute of Foundation Models

We are a dedicated research lab for building, understanding, using,
and risk-managing foundation models. Our mandate is to advance
research, nurture the next generation of AI builders, and drive
transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the
core of cutting-edge foundation model training, alongside
world-class researchers, data scientists, and engineers, tackling
the most fundamental and impactful challenges in AI
development. You will participate in the development of
groundbreaking AI solutions that have the potential to reshape
entire industries. Strategic and innovative problem-solving skills
will be instrumental in establishing MBZUAI as a global hub for
high-performance computing in deep learning, driving impactful
discoveries that inspire the next generation of AI pioneers.

The Role

We are the AllWorld Team under the Institute of
Foundation Model (IFM) at MBZUAI . At AllWorld, we are
pioneering the development of the PAN (Physical, Agentic, and
Networked) world models —the next-generation foundation models
to unlock machine intelligence beyond lingual.

Our mission is to tackle the fundamental challenges of world
modeling and establish a new paradigm for next-generation
machine reasoning . We are looking for passionate individuals
who share our vision and are eager to push the boundaries of AI
together.

Key Responsibilities

• Develop the foundational world model to accurately simulate the

physical world.

• Collaborate with engineering and data teams to tackle key

challenges in training the world model on large-scale
clusters.

• Develop metrics and evaluation benchmarks to better assess

model performance.

• Design and implement a scalable and efficient data annotation

pipeline to ensure high-quality labeled data for training and
evaluation.

• Optimize inference efficiency to enable real-time

interaction.

Areas of Focus

• Scalable Training Systems : Develop and optimize

infrastructure for training multimodal LLMs and video diffusion
models at massive scale.

• Efficient Data Pipelines : Build scalable video data

pipelines and annotation frameworks to support high-quality
training data.

• Inference Optimization : Enhance inference efficiency

through optimization and distillation techniques to enable
real-time interaction.

• Visual Tokenization : Develop methods for discretizing

visual features into tokens for improved model representation.

• Quantitative Evaluation : Establish rigorous benchmarks

to assess physical accuracy, controllability, and
intelligence.

• Scaling Laws for Video Pretraining : Investigate scaling

law principles to guide efficient video pre-training
strategies.

Academic Qualifications

• MSc or PhD in Machine Learning or Computer Science, or

equivalent industry experience.

Professional Experience

• Experience in large-scale model training (LLMs or Diffusion

Models) on large clusters.

• Hands-on experience with state-of-the-art video generative

models (e.g., Sora, Veo2, MovieGen, CogVideoX, etc.).

• Experiences in building and optimizing large-scale video data

pipelines.

• Experience in accelerating diffusion model inference for

improved efficiency.

• Exceptional problem-solving and troubleshooting skills to

tackle complex technical challenges.

• Strong systems and engineering expertise in deep learning

frameworks such as PyTorch.

• Strong communication and collaboration skills for effective

cross-functional teamwork.

• Ability to navigate ambiguity and drive projects in rapidly

evolving research areas.

• Research contributions to top-tier conferences or journals

(e.g., ICML, ICLR, NeurIPS, ACL, CVPR, COLM, etc.), with published
work in relevant domains.

$150,000 - $450,000 a year

Salary Range

The posted salary range represents the company’s good faith
estimate of the compensation for this position upon hire. The
actual compensation offered may vary within this range depending on
individual qualifications, including but not limited to relevant
skills, experience, education, certifications, geographic location,
and specific business needs.

Benefits Include

*Comprehensive medical, dental, and vision benefits

*Bonus

*401K Plan

*Generous paid time off, sick leave and holidays

*Paid Parental Leave

*Employee Assistance Program

*Life insurance and disability

We may use artificial intelligence (AI) tools to support parts of
the hiring process, such as reviewing applications, analyzing
resumes, or assessing responses and identifying potential
inconsistencies or verification signals in application materials
based on available information. These tools assist our recruitment
team but do not replace human judgment. Final hiring decisions are
ultimately made by humans. If you would like more information about
how your data is processed, please contact us.

About the Institute of Foundation Models

We are a dedicated research lab for building, understanding, using,
and risk-managing foundation models. Our mandate is to advance
research, nurture the next generation of AI builders, and drive
transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the
core of cutting-edge foundation model training, alongside
world-class researchers, data scientists, and engineers, tackling
the most fundamental and impactful challenges in AI
development. You will participate in the development of
groundbreaking AI solutions that have the potential to reshape
entire industries. Strategic and innovative problem-solving skills
will be instrumental in establishing MBZUAI as a global hub for
high-performance computing in deep learning, driving impactful
discoveries that inspire the next generation of AI pioneers.

The Role

We are the AllWorld Team under the Institute of
Foundation Model (IFM) at MBZUAI . At AllWorld, we are
pioneering the development of the PAN (Physical, Agentic, and
Networked) world models —the next-generation foundation models
to unlock machine intelligence beyond lingual.

Our mission is to tackle the fundamental challenges of world
modeling and establish a new paradigm for next-generation
machine reasoning . We are looking for passionate individuals
who share our vision and are eager to push the boundaries of AI
together.

Key Responsibilities

• Develop the foundational world model to accurately simulate the

physical world.

• Collaborate with engineering and data teams to tackle key

challenges in training the world model on large-scale
clusters.

• Develop metrics and evaluation benchmarks to better assess

model performance.

• Design and implement a scalable and efficient data annotation

pipeline to ensure high-quality labeled data for training and
evaluation.

• Optimize inference efficiency to enable real-time

interaction.

Areas of Focus

• Scalable Training Systems : Develop and optimize

infrastructure for training multimodal LLMs and video diffusion
models at massive scale.

• Efficient Data Pipelines : Build scalable video data

pipelines and annotation frameworks to support high-quality
training data.

• Inference Optimization : Enhance inference efficiency

through optimization and distillation techniques to enable
real-time interaction.

• Visual Tokenization : Develop methods for discretizing

visual features into tokens for improved model representation.

• Quantitative Evaluation : Establish rigorous benchmarks

to assess physical accuracy, controllability, and
intelligence.

• Scaling Laws for Video Pretraining : Investigate scaling

law principles to guide efficient video pre-training
strategies.

Academic Qualifications

• MSc or PhD in Machine Learning or Computer Science, or

equivalent industry experience.

Professional Experience

• Experience in large-scale model training (LLMs or Diffusion

Models) on large clusters.

• Hands-on experience with state-of-the-art video generative

models (e.g., Sora, Veo2, MovieGen, CogVideoX, etc.).

• Experiences in building and optimizing large-scale video data

pipelines.

• Experience in accelerating diffusion model inference for

improved efficiency.

• Exceptional problem-solving and troubleshooting skills to

tackle complex technical challenges.

• Strong systems and engineering expertise in deep learning

frameworks such as PyTorch.

• Strong communication and collaboration skills for effective

cross-functional teamwork.

• Ability to navigate ambiguity and drive projects in rapidly

evolving research areas.

• Research contributions to top-tier conferences or journals

(e.g., ICML, ICLR, NeurIPS, ACL, CVPR, COLM, etc.), with published
work in relevant domains.

$150,000 - $450,000 a year

Salary Range

The posted salary range represents the company’s good faith
estimate of the compensation for this position upon hire. The
actual compensation offered may vary within this range depending on
individual qualifications, including but not limited to relevant
skills, experience, education, certifications, geographic location,
and specific business needs.

Benefits Include

*Comprehensive medical, dental, and vision benefits

*Bonus

*401K Plan

*Generous paid time off, sick leave and holidays

*Paid Parental Leave

*Employee Assistance Program

*Life insurance and disability

We may use artificial intelligence (AI) tools to support parts of
the hiring process, such as reviewing applications, analyzing
resumes, or assessing responses and identifying potential
inconsistencies or verification signals in application materials
based on available information. These tools assist our recruitment
team but do not replace human judgment. Final hiring decisions are
ultimately made by humans. If you would like more information about
how your data is processed, please contact us.