Research Engineer I (AI Evaluation, Trust Technologies)

Nanyang Technological University · Singapore

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Nanyang Technological University’s National Centre for Research in Digital Trust (DTC) is a Trust Technology Research Centre to execute a national program to help put Singapore into a strong trust hub. The key objective is to support efforts to create a trusted digital environment for its people and businesses by providing businesses and consumers with greater assurance and confidence as they digitalize.

We are looking for a Research Engineer to support the evaluation and validation of AI systems developed at the Centre. The role focuses on assessing how reliably these systems perform in real-world conditions — designing evaluation methods, building test datasets, analysing where and why models succeed or fail, and contributing to the development of prototypes and tooling. This work underpins the Centre's mission of ensuring that emerging technologies are trustworthy, fair, and safe in practice.

Key Responsibilities:

The National Centre for Research in Digital Trust (DTC) at NTU is seeking a Research Engineer to support the evaluation and validation of AI systems. This role combines hands-on technical work with regular engagement with external partners. You will:

• Work directly with project partners and clients — participating in requirement discussions and technical meetings, preparing materials, and following up on action items.
• Design evaluation frameworks, test datasets, and scoring methodology for AI and ML systems.
• Develop annotation guidelines and monitor labelling quality.
• Conduct error analysis and translate findings into clear recommendations for improvement.
• Run structured experiments with large language models, and build supporting prototypes and tooling.
• Communicate results clearly to both technical and non-technical audiences.

Job Requirements:

• Bachelor’s degree in computer science, engineering, data science, or a related field.

• Confidence to engage directly with external partners and clients, and the ability to explain technical concepts clearly to non-technical audiences.

• Proficiency in Python, with practical experience in ML workflows including model evaluation and error analysis.

• Working knowledge of NLP and hands-on experience with large language models.

• Experience in evaluation methodology or data annotation will be a strong advantage.

• Careful, methodical, and able to work independently under tight timelines.

We regret that only shortlisted candidates will be notified.

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