Job no: 498903
Department: Infocomm Technology
Contract type: Contract
As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.
The SIT x NVIDIA AI Centre (SNAIC) is seeking a capable and hands-on Research Engineer to lead software development across a range of AI-driven research and industry projects. This role involves designing, building, and scaling production-grade systems and tools that support applied AI workflows, including model integration, data processing, visualization, deployment infrastructure, and user-facing applications where relevant, delivering deployment-ready Minimum Viable Products (MVPs). You’ll work closely with AI researchers and cross-functional teams to translate experimental prototypes into stable, maintainable, and scalable software systems. The development scope spans frontend, backend, APIs, dashboards, CLI tools, and system-level integration, depending on project needs. We’re looking for someone with strong engineering fundamentals, product thinking, and a builder’s mindset who thrives in a fast-moving, lean team. Ideal for someone who wants end-to-end ownership, diverse technical exposure, and the opportunity to shape impactful systems with real-world adoption.

JOB REQUIREMENTS:

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field
• Strong proficiency in one or more programming languages (e.g. Python, TypeScript, C++, .Net, Php)
• Solid experience in software architecture, data structures, and design patterns
• Familiarity with at least one modern web frontend or full-stack framework (e.g. React, Vue, Angular, NextJs, Tailwind)
• Experience with Databases such as Microsoft SQL, PostgreSQL, MongoDB etc.
• Experience with REST APIs, microservices, and system integration
• Working knowledge of Git and collaborative development workflows
• Familiarity with DevOps practices (Docker, CI/CD, cloud environments) is advantageous
• Understanding of application security principles, secure coding practices, and common vulnerabilities (e.g. OWASP Top 10, authentication/authorization, data encryption) will be advantageous
• Experience working on AI/ML systems, data pipelines, or deployment tools will be advantageous

• Design and implement modular, reusable, and scalable software systems across frontend and backend stacks
• Collaborate with AI engineers to integrate models into applications and pipelines
• Build interfaces, internal tools, and utilities that improve workflow efficiency and project deployment
• Maintain code quality, documentation, and test coverage across projects
• Contribute to CI/CD pipelines, containerization, and deployment setups where relevant
• Participate in code reviews, design discussions, and architectural decisions
• Support iterative prototyping and productisation of AI research outputs