Research Scientist

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.