aimlroles

Research Scientist - The Diffusion LLM Team

Institute of Foundation Models Open · verified Oct 22, 2026
On-site Sunnyvale Full-time Mid-level AI Research

At a glance

Mid-level AI Research role at Institute of Foundation Models. Sunnyvale · full-time · $150,000–$400,000 base.

SalaryStated by Institute of Foundation Models
$150,000 – $400,000
Base salary for this role, in USD per year, as published in the posting.
This role, from Institute of Foundation ModelsOur estimate for the market: $195,000–$300,000Median $274,500
The range includes the market median of $274,500 for mid-level AI research roles in Sunnyvale.
Worked out from 7 postings that stated pay, median $274,500. Last updated Oct 6, 2026.
RoleAI Research
SeniorityMid-level
LocationSunnyvale
WorkplaceOn-site
EmploymentFull-time
Base pay$150,000–$400,000
PostedApr 27, 2026 · 5mo ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Train and scale language models for research and practical deployment
  • Help develop and release diffusion-based language models at industrial scale
  • Test training approaches and objectives that make model scaling more efficient
  • Publish research and contribute models or code to open-source releases

What you bring

  • A graduate degree in machine learning or computer science, or comparable industry experience
  • Hands-on work training large models at scale with current deep learning frameworks
  • Strong experience with transformer architectures, large-scale optimization, and LLM training focused on scaling
  • Research outputs such as publications, open-source contributions, or models you have released

Who this fits

This role suits a researcher who can work independently and contribute within a collaborative team. It is an onsite position in Sunnyvale.

From the employer

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

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.