Research Fellow (Machine Learning Systems)

National University of Singapore; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

The National University of Singapore invites applications for a Research Fellow to work on large-model optimization and software-hardware co-design of AI computing systems. The role explores how models are trained and run efficiently, balancing computation, memory, and communication with hardware considerations.

Fellow will develop methods to reduce resource use while preserving model quality, and will collaborate with colleagues across ML, computer systems, and architecture to improve

Qualifications

• PhD required in CS, ECE or closely related field.
• Experience in efficient deep learning, ML systems, compilers, parallel and distributed computing, or computer architecture is preferred.
• Strong programming abilities and ability to turn ideas into working implementations and experiments.

Responsibilities

• Conduct original research in large-model optimization and software-hardware co-design of AI computing systems.
• Identify bottlenecks in training and inference, develop methods, and assess trade-offs between model quality and efficiency.
• Build prototypes and evaluate methods on computing platforms or architectural simulations.
• Publish results and collaborate with ML, systems, and computer architecture teams.

Skills

Programming

Independent research

Communication of findings

ML/AI systems experience

Education

PhD in Computer Science or Electrical and Computer Engineering

Job description

Company description:

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job description:

Job Description

The Department of Electrical and Computer Engineering at the National University of Singapore invites applications for a Research Fellow to work with Professor SHANG Li on large-model optimization and software-hardware co-design of AI computing systems.

Our research asks how large models can be trained and run more efficiently, and how computing systems should be designed to support them. We study these questions together: the way a model is computed shapes its hardware requirements, while the capabilities and limits of the hardware influence how the model should be implemented.

The fellow will develop methods that reduce the computation, memory, and communication required for large-model training and inference while preserving model quality. The work will also explore how algorithms, system software, and hardware architectures can be designed together to improve the performance and energy efficiency of AI computing systems.

Responsibilities include:

• Conduct original research in large-model optimization and software-hardware co-design of AI computing systems.
• Identify bottlenecks in training and inference, develop new methods to address them, and explain the trade-offs between model quality and computing efficiency.
• Build prototypes and evaluate proposed methods through experiments on computing platforms or architectural simulation, as appropriate to the research.
• Publish research findings and collaborate with colleagues in machine learning, computer systems, and computer architecture.

Job Requirements

• A PhD in Computer Science, Electrical and Computer Engineering, or a closely related field.
• Research experience in one or more relevant areas, such as efficient deep learning, machine learning systems, compilers, parallel and distributed computing, or computer architecture.
• Strong programming skills and the ability to turn research ideas into working implementations and rigorous experiments.
• The ability to pursue independent research, communicate findings clearly, and collaborate across software and hardware research.