MSc or PhD in Machine Learning, Computer Science, or equivalent experience
Hands-on experience in training large models with deep learning frameworks
Strong understanding of transformer architectures and optimization techniques
Proven expertise in LLM pre-training or post-training practices
Research publications or open-source contributions in relevant fields
Familiarity with diffusion models is a plus but not mandatory
Responsibilities
Design, train, and scale large language models for both research and practical applications
Lead or assist in releasing industrial-scale diffusion language models
Develop and assess training strategies for effective model scaling
Publish research outcomes and support open-source model/code initiatives
Benefits
Collaborative environment with cross-disciplinary teams
Opportunity to contribute to cutting-edge research
Access to industrial-scale resources for model training
Involvement in impactful open-source projects
Full Job Description
The Role
As a member of the Diffusion LLM Team at MBZUAI, you will play a central role in designing, building, and releasing industrial-scale Diffusion Large Language Models. Our team has two core missions. First, we develop and release diffusion-based LLMs that push the speed-quality frontier at scale by matching autoregressive model quality while enabling faster generation. Second, we improve inference-time scaling relative to standard LLMs, so that additional test-time compute translates into higher-quality samples.
You will work closely with researchers and engineers across architecture, training, and infrastructure to turn research ideas into high-impact model releases for next-generation LLMs.
Key Responsibilities
Design, train, and scale large language models for research and real-world deployment.
>
Lead or contribute to the release of industrial-scale diffusion language models.
>
Develop and evaluate training strategies and objectives for efficient model scaling.
>
Publish research findings and contribute to open-source model and code releases.
>
Academic Qualifications
MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience.
Professional Experience
Hands-on experience training large models using modern deep learning frameworks at scale.
>
Strong background in transformer architectures and large-scale optimization techniques.
>
Demonstrated expertise in LLM pre-training or post-training, with a strong focus on model scaling.
>
Research track record evidenced by publications, open-source contributions, or released models.
>
Knowledge of diffusion models or discrete diffusion methods is a plus, but not required.
>
Ability to work independently while contributing effectively to a collaborative research team.
