About the role
Research Associate (Artificial Intelligence / Machine Learning / Robotics) is a active engineering role at ntu in NTU Main Campus, Singapore. Open the role to review the official description and apply on the company site.
CompanyOnsite
Key Responsibilities
- Develop accelerated AI/ML and robotics algorithms that significantly reduce computation cost, memory footprint, and power consumption.
- Design and optimize efficient training and inference pipelines for foundation models (LLM, VLM, VLA) across different model sizes and deployment settings.
- Apply and advance model compression techniques, including quantization, pruning, knowledge distillation, low-rank adaptation, and related methods.
- Conduct algorithm-hardware co-design to enable efficient and accurate deployment of AI algorithms on robotic, edge, and heterogeneous computing platforms.
- Develop methods for efficient deployment of AI models on cloud and edge devices, considering latency, throughput, and energy constraints.
- Implement, evaluate, and benchmark accelerated models using rigorous experimental protocols.
- Contribute to research publications in top-tier AI, ML, and robotics conferences and journals.
- Collaborate with interdisciplinary teams spanning AI, systems, and robotics.
- Contribute to open-source codebases and reproducible research practices.
Requirements
- A Master’s degree in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a closely related field.
- Strong research background in AI and machine learning, with a focus on efficient or accelerated models.
- Proven experience with model compression techniques, such as quantization, pruning, distillation, and low-rank adaptation.
- Demonstrated experience working with foundation models, particularly vision-language models (VLMs); experience with LLMs or VLAs is a strong advantage.
- Solid understanding of algorithm-hardware co-design, especially for robotics or edge AI deployment.
- Strong programming skills in C, C++, and Python, with experience in deep learning frameworks such as PyTorch or TensorFlow.
- Familiarity with deployment constraints on cloud, edge, or embedded systems.
- , embodied AI, or autonomous systems is an advantage.
- Strong publication record or clear potential to publish in leading international venues.
- Ability to work independently, manage complex research tasks, and collaborate effectively in interdisciplinary teams.
- Good written and oral communication skills.
- We regret to inform that only shortlisted candidates will be notified.
- Hiring Institution: NTU