Research Engineer (AI Accelerator and Energy-Efficient Computing)

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

The National University of Singapore is seeking a Research Engineer to develop next-generation energy-efficient AI hardware. The role involves implementing strategies for GPU-based AI workloads, designing performance monitoring frameworks, and deploying machine learning models for optimal voltage-frequency operating points. Candidates should have a degree in Computer Engineering or a related field, strong programming skills in Python and C/C++, and familiarity with deep learning models. The position offers opportunities for mentorship and research publication.

Qualifications

• Bachelor’s or Master’s degree in Computer Engineering, Computer Science, or a related field.
• Strong programming skills in Python and C/C++.
• Familiarity with transformer architectures and large language models is advantageous.

Responsibilities

• Develop fine-grained dynamic voltage and frequency scaling strategies for GPU‑based AI workloads.
• Design a high-resolution performance monitoring framework for real-time optimization.
• Develop machine learning models to predict optimal voltage-frequency operating points.
• Integrate predictive control algorithms for adaptive power management.
• Collaborate on hardware–software co‑design for AI acceleration.
• Contribute to prototype development and performance evaluation.
• Publish research findings and mentor undergraduate students.

Skills

Python

C/C++

Machine learning frameworks (PyTorch, TensorFlow)

System-level optimization

Analytical skills

Problem-solving

Communication skills

Adaptability

Education

Bachelor’s or Master’s degree in Computer Engineering, Computer Science, or related field

Job description

Job Title: Research Engineer (AI Accelerator and Energy‑Efficient Computing)

Location: Kent Ridge Campus

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Electrical and Computer Engineering

Employee Category: Research Staff

Job Description

We are seeking a highly motivated and skilled Research Engineer to join our team in developing next‑generation energy‑efficient AI hardware. The project aims to demonstrate an AI accelerator chiplet that integrates emerging non‑volatile memory (RRAM or MRAM) with conventional I/O interfaces. This platform will showcase compute‑memory co‑design advantages, including enhanced data throughput, improved energy efficiency, and reduced data‑movement overheads for large‑scale AI workloads.

Key Responsibilities

• Develop and implement fine‑grained dynamic voltage and frequency scaling (DVFS) strategies tailored for GPU‑based AI workloads, with control at the level of transformer blocks and attention layers.
• Design and build a high‑resolution (microsecond‑scale) performance monitoring framework to capture workload intensity, memory access patterns, and GPU utilization for real‑time optimization.
• Develop and deploy machine learning models to predict optimal voltage‑frequency operating points during LLM training and inference.
• Integrate predictive control algorithms with hardware platforms to enable real‑time, adaptive power management across heterogeneous compute and memory subsystems.
• Collaborate on system‑level hardware–software co‑design to realize energy‑efficient AI acceleration.
• Contribute to prototype development, benchmarking, and evaluation of performance and energy efficiency.
• Publish research findings in leading journals and present at international conferences.
• Mentor undergraduate students and support team‑based research activities.

Qualifications

• Bachelor’s or Master’s degree in Computer Engineering, Computer Science, or a related field.
• Strong programming skills in Python and C/C++, with experience in machine learning frameworks such as PyTorch or TensorFlow.
• Familiarity with deep learning models—particularly transformer architectures and large language models (LLMs)—is advantageous.
• Experience or interest in system‑level optimization (e.g., DVFS, workload profiling, or power management) is a plus.
• Strong analytical and problem‑solving skills, with the ability to conduct independent research and contribute to technical publications.
• Ability to work effectively in a multidisciplinary environment, with good communication skills, initiative, and adaptability.