methodologies.

Key Responsibilities

• Lead the research of technology for improving the efficiency of Large Language Model (LLM) while performing target capabilities or supporting many capabilities, such as novel architectures and improved pre-training.
• Design and implement NLP algorithms for model training and prediction, leverage ML infrastructure, and contribute to model optimization and data processing, using Pytorch or other frameworks.
• Integrate and improve LLM algorithms to work with other models such as computer vision models
• Identify defined problems/gaps in existing technology and engage other Research teams, stakeholders and leaders to expand efficient LLM technology.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies.
• Write up results in design documents, technical reports, and papers for publication.
• Represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
• Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.

Academic Qualifications

Minimum

Master’s in Computer Science, a related technical field, or equivalent practical experience.

Preferred

PhD or equivalent research experience in Natural Language Processing

Minimum Professional Experience

• Experience with state-of-the-art Gen AI techniques and models (e.g., LLMs, Multi-Modal, Large Vision Models) or with Gen AI-related concepts (e.g., language modeling, computer vision).
• Experience with software development in one or more programming languages (e.g. Python, C++), and with data structures/algorithms.
• Excellent problem-solving and troubleshooting skills to address complex technical challenges.
• Effective communication and collaboration skills to work with cross functional teams.
• Ability to effectively navigate ambiguity.

Preferred Professional Experience

• Experience leading research efforts and influencing other researchers.
• Experience with efficiency, modularity or related topics for LLMs.
• Experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Experience with design and optimization of algorithms in performance constrained environments (e.g., mobile).
• Experience in innovative research, contributing to research communities including publishing in forums (e.g., ACL, EMNLP, NAACL, EACL, COLING, ICLR, AAAI, NeurIPS).