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 Research Scientist with a focus on data-centric large language model (LLM) development, your role will center on advancing the frontiers of how LLMs reason, retrieve, and interact with external information sources. You will proactively identify, collect, and organize datasets that enable LLMs to perform complex reasoning tasks, while also developing scalable systems and tooling that integrate cutting-edge research with robust engineering. Your work will have a direct impact on the performance and reliability of intelligent systems at MBZUAI IFM.
Key Responsibilities
Lead research and implementation of reasoning-enhanced LLM capabilities through novel data collection, architecture design, and system integration.
Design and implement pipelines to collect, curate, and structure open-source and web-scale data relevant to reasoning tasks, ensuring scalability and reproducibility.
Build robust software to support fine-tuning, evaluation, and deployment of LLMs that interact with structured and unstructured knowledge bases.
Collaborate with ML researchers to create, test, and evaluate new approaches in information retrieval, agentic search, and RAG (retrieval-augmented generation) pipelines.
Rapidly prototype tools, APIs, and infrastructure for enabling LLMs to reason over external information, and build datasets for identifying and analyzing LLM failure modes.
Communicate research findings in internal documents and external publications (e.g., top-tier conferences like ACL, ICLR, NeurIPS).
Contribute to design/code reviews and foster engineering best practices in a high-performance research environment.
Represent MBZUAI at conferences and forums, promoting institutional leadership in safe, efficient, and high-impact AI systems.
Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Academic Qualifications
Master’s in Computer Science, Data Science, or a related technical field, or equivalent practical experience required.
PhD or equivalent research experience in Machine Learning, NLP, or Data Science with a focus on reasoning and LLMs preferred.
Minimum Professional Experience
Experience working with large language models, including fine-tuning, prompt engineering, and multi-modal interaction.
Strong Python development skills with a focus on research-grade code and scalable data pipelines.
Familiarity with collecting and processing large-scale datasets from open-source and web resources.
Demonstrated ability to work with ML infrastructure (e.g., model evaluation, optimization, debugging).
Proactive mindset with the ability to identify impactful research questions and execute on them with minimal supervision.
Effective communication and collaboration skills for working in cross-functional teams.
Preferred Professional Experience
Experience designing and deploying agentic LLM systems, reasoning benchmarks, or RAG pipelines.
Background in building complex knowledge retrieval systems (e.g., knowledge graphs, semantic search, indexing).
Strong publication record in leading AI conferences (e.g., ICLR, ACL, NeurIPS, EMNLP).
Familiarity with performance constraints in production environments and the trade-offs in model and data design.
Prior contributions to open-source ML research or data tools.
