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 productized 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:
- Bachelor’s degree in computer science/ engineering or related fields.
- Proficiency in Python; experience with Java, C/C++, Go, and PyTorch/TensorFlow is a plus.
- Proficient with Linux (e.g., Ubuntu, CentOS) and shell scripting with experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes). Familiarity with version control systems (Git) and collaborative development tools.
- Experience in developing and deploying machine learning models, especially in NLP and LLMs, including tasks like data preprocessing, feature extraction, and model training/evaluation. Strong knowledge of machine learning, deep learning (e.g., knowledge graphs, AI testing, machine unlearning).
- Experience in end-to-end ML system development (data exploration, feature engineering, model training/evaluation) with strong understanding of software engineering principles and best practices as well as attention to ethical considerations in AI, including data privacy and security.
- Ability to communicate technical concepts to both technical and non-technical stakeholders, with strong analytical, problem-solving, and collaborative skills
- Self-motivated, able to work independently in a fast-paced environment.
We regret to inform that only shortlisted candidates will be notified.
