Applied AI Engineer - Multimodal Systems for Healthcare

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 90,000 - 150,000; Full time

Benefits offered by this job

World-class computational infra

Collaboration with leading researchers

Career development opportunities

Job summary

A*STAR RESEARCH ENTITIES in Singapore is seeking a Research Engineer to develop and deploy advanced AI systems. You will translate cutting-edge research into scalable real-world applications, collaborating with researchers to implement and optimize ML models across healthcare and biomedical domains.

The role blends software engineering, machine learning, and systems engineering to bridge research and deployment, including building data pipelines, model serving platforms, and scalable

Qualifications

• Bachelor's or Master's degree in CS, CE, AI, DS or related disciplines.
• Strong Python programming skills.
• Experience with PyTorch or TensorFlow and software engineering practices.

Responsibilities

• Design, develop, and maintain AI systems and pipelines.
• Implement state-of-the-art AI algorithms from research to production.
• Build scalable data processing and model training pipelines.
• Develop multimodal AI systems combining text, image, video, and structured data.
• Develop APIs, backend services, and model-serving platforms.
• Support experimentation, model evaluation, and performance optimization.

Skills

Python programming

PyTorch

TensorFlow

Software engineering

Linux familiarity

Problem solving

Education

Bachelor's or Master's degree in CS/AI/related

Tools

Docker

Kubernetes

Git

AWS

Azure

GCP

Job description

A*STAR RESEARCH ENTITIES in Singapore is seeking a Research Engineer to develop and deploy advanced AI systems. You will translate cutting-edge research into scalable real-world applications, collaborating with researchers to implement and optimize ML models across healthcare and biomedical domains.

The role blends software engineering, machine learning, and systems engineering to bridge research and deployment, including building data pipelines, model serving platforms, and scalable