"Research Assistant (Full-Stack Development)"

Research Assistant (Full-Stack Development)

Posting Start Date: 04/03/2026

A/P Chen Ying from Mathematics Department, National University of Singapore is seeking a highly motivated and technically proficient Research Assistant (Full-Time) to lead the end-to-end design and development of interactive systems for cutting-edge optimization and machine learning (ML) models. This role is pivotal in "translating" complex academic research into functional, high-fidelity web applications and interfaces. You will work at the intersection of applied software engineering, user-centered design, and AI systems.

Major Responsibilities

• Full-Stack Development: Lead the design and implementation of intuitive UIs and robust backend architectures for ML models.
• System Integration: Build and maintain data collection pipelines and experimental setups that bridge backend mathematical models with frontend interfaces.
• Architecture & Scaling: Maintain clean, scalable codebases and assist in the deployment of research tools to cloud or edge environments.
• Rapid Prototyping: Develop high-fidelity UI prototypes through iterative, user-centered design cycles.
• Collaboration: Actively participate in lab meetings and workshops with professors, research fellows, and industry partners.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Engineering or a related technical field.
• Strong experience with modern web frameworks, with a preference for React.js.
• Proficiency in Python and experience with backend frameworks such as FastAPI, Flask, or Django.
• Familiarity with relational databases (e.g., PostgreSQL, MySQL) or NoSQL solutions (e.g., MongoDB).
• Proficiency in version control (Git) and modern development environments (VS Code, PyCharm, etc.).
• Ability to explain technical trade-offs to a multidisciplinary team.

Preferred (Bonus) Skills

• Experience in designing and consuming APIs Design.
• Experience with human-centered design
• Exposure to machine learning pipelines (Scikit-learn, PyTorch, or TensorFlow) and model deployment.
• Experience with containerization (Docker) and cloud services (AWS, Firebase, or similar).

More Information

Location: Kent Ridge Campus

Organization: Science

Department: Mathematics

Job requisition ID: 31952

Apply now