Research Engineer II (Trust Technologies)

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 people and businesses in the digital transformation by providing businesses and consumers with greater assurance and confidence as they digitalize. For more details, please view https://www.ntu.edu.sg/dtc

We are looking for a Research Engineer to develop, implement, and oversee techniques and systems in Trust Technology and related areas such as privacy, security and blockchain, which are important components of digital trust platforms. The role will focus on emerging technologies that engenders trust and espouse the values of fairness, safety, and privacy in digital technologies.

Key Responsibilities:

• Conduct research into trust technologies testing – translating algorithms, tools, and frameworks into working prototypes that can explain how research outputs can be productised into new capabilities.

• Work closely with Centre’s researchers to design and develop system implementation work from research into the product.

• Design and build working tools that can support the technology transfer of new capabilities to research partners and can be used to showcase the value of a given research outcome.

• Write and maintain technical documentation, presentations, and papers on research into trust technologies testing, helping to educate and raise the overall competency in emerging areas of trust technologies.

• Engage global partners and researchers to understand latest trends and advance Singapore’s mindshare in this domain.

Job Requirements:

• Master’s degree in computer science/ engineering or related fields.

• Proficiency in Python is essential; experience with Java, C/C++, Go, and PyTorch/TensorFlow is a plus. Proficient with Linux (e.g., Ubuntu, CentOS) and shell scripting.

• Experience in developing and deploying machine learning models, especially in NLP and LLMs, covering tasks like data preprocessing, feature extraction, training, and evaluation.

• Strong knowledge of machine learning and deep learning techniques (e.g., knowledge graphs, machine unlearning, AI testing). Cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) experience required.

• Familiarity with Git and collaborative development tools with experience in end-to-end ML system development (data exploration, feature engineering, model training/evaluation) is advantageous.

• Solid understanding of software engineering principles and best practices and the ability to explain complex technical concepts to both technical and non-technical audiences.

• Strong analytical, problem-solving, and innovative thinking skills and a good understanding of ethical AI practices, especially related to data privacy and security.

• Self-motivated, able to work independently in a fast-paced environment with strong collaboration skills. Experience working in cross-functional teams with attention to detail and commitment to delivering high-quality results.

• Experience in research on mechanistic interpretability.

• Experience in the implementation of multi-modal LLMs or vision-language models.

We regret to inform that only shortlisted candidates will be notified.