Machine Learning Engineer
US$150,000 – US$450,000 / yr base salary
Posted 5 May 2026
Advertised as “Machine Learning Engineer – World Model”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
- Visa sponsorship
- Offered
| Base salary | US$150,000 – US$450,000 / yr |
|---|
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 Team
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.
Role Overview
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We’re looking for a Machine Learning Engineer focused on ML infrastructure and MLOps to design and operate the systems that power our research environment. You’ll build scalable, reliable, and observable cloud infrastructure, working closely with researchers to support data pipelines, experimentation, and evaluation workflows.
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This role balances fast-moving research needs with production-grade systems, ensuring that experimental work can scale reliably when needed.
Key Responsibilities
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Design, build, and operate scalable ML infrastructure on AWS (e.g., compute, storage, networking, access control).
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Develop and maintain MLOps workflows for data versioning
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Build and manage distributed systems for large-scale data processing (filtering, captioning, etc.) and model evaluation.
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Own architecture decisions for ML infrastructure and drive best practices in reliability, scalability, and cost efficiency.
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Implement observability across systems, including monitoring, logging, and alerting.