Research Engineer (AI Accelerator and Energy-Efficient Computing)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Engineer-%28AI-Accelerator-and-Energy-Efficient-Computing%29/32425-en_GB/

We regret that only shortlisted candidates will be notified.

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.