Visiting Student - Efficient and Distributed Machine Learning

Description

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is the world’s first AI-focused university. Founded in 2019, MBZUAI is a research-oriented graduate-level university that has grown to host over 80 world-class faculty, over 330 graduate-level students, and is already ranked in the world’s top 25 by CSRankings for AI-related fields. Located in Abu Dhabi, the university aims to become a global leader in AI education and research by providing cutting-edge programs in Artificial Intelligence, empowering students to shape the future of technology. MBZUAI is committed to fostering innovation, collaboration, and ethical practices in AI to address global challenges.

About the role: 

We are seeking motivated visiting students to join our team in the areas of efficient training and distributed machine learning. The role requires working with faculty, postdoctoral researchers, and graduate students on ongoing projects with both theoretical depth and practical applications.

This is an opportunity to:

• Contribute to cutting-edge research in topics such as optimization, quantization, pruning, federated learning, and communication-efficient training.
• Gain exposure to theoretical and empirical research workflows in machine learning.
• Work on publications or open-source tools depending on background and interest.

This role is ideal for students considering future graduate study or a research career in ML/AI.

Key Responsibilities:

• Assist in designing and evaluating algorithms for efficient and distributed ML training.
• Conduct experiments using standard benchmarks and real-world datasets.
• Participate in reading groups, code reviews, and collaborative research discussions.
• Contribute to publications or codebases under the supervision of faculty or senior researchers.

Qualifications

Minimum:

Currently enrolled in a Bachelor's, Master’s, or Ph.D. program in Computer Science, Engineering, Applied Mathematics, or a related field.

• Strong programming skills in Python and familiarity with ML libraries (e.g., PyTorch, JAX).
• Solid understanding of machine learning fundamentals, especially in one or more of: optimization, generalization, federated learning, or model compression.
• Ability to work independently and collaboratively in a research environment.

Preferred:

• Prior research or internship experience in ML or optimization.
• Experience contributing to open-source projects or academic research.
• Familiarity with distributed computing or large-scale training environments.

What we offer:

• Opportunity to work at a top-tier AI research institution with leading researchers.
• Mentorship and collaboration on high-impact, publishable projects.
• Access to cutting-edge compute resources and research tools.
• Return-trip flight
• Accommodation (or housing allowance)
• Health insurance and visa sponsorship
• A stipend of AED 8,000 – 10,000 per month based on the applicant's education

Application Instructions

To apply, please submit:

1. A cover letter expressing your interest and goals for the internship.
2. Curriculum Vitae (C.V).
3. A link to your GitHub and/or Google Scholar profile (if available).
4. (Optional) A short paragraph describing a project or topic you'd be interested in exploring.

Applications are reviewed on a rolling basis. Early applications are encouraged. 

For more information or to apply, please visit MBZUAI Careers.

Equal Employment Opportunity Statement

Mohamed bin Zayed University of Artificial Intelligence shall abide by the requirements of 41 CFR §§ 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, or national origin. Moreover, these regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, national origin, protected veteran status or disability.