Job Description

The Singapore national program for the utilization of Artificial Intelligence (AI) in Drug Discovery (AIDD), funded by the Singapore National Research Foundation (NRF) and hosted by the Singapore Agency for Science, Technology and Research (A*STAR) seeks a talented and motivated Scientific Platform Engineer to join our initiative. AIDD will develop novel machine learning models and algorithms towards the discovery and validation of drug targets, biomarkers, and molecular entities as therapeutics.

As a scientific platform engineer, you will play a pivotal role in collaborating with scientific domain experts across AIDD to translate research prototypes and scientific workflows into a robust, scalable, enterprise-grade software system that is able to leverage various data modalities. This position is ideal for engineers who thrive in working across scientific disciplines, building infrastructure for data-intensive computational research to enable scientists and other end users to apply AI and computation at scale.

Key Responsibilities

Scientific Collaboration

• Work closely with principal investigators (PIs) and research teams across the AIDD organization to understand the computational and experimental workflows.
• Translate scientific requirements into robust software architectures and production systems.

Research-to-Engineering

• Transform research code, prototypes, and experimental pipelines into scalable and maintainable software platforms.
• Develop reusable tools and services that enable researchers to operationalize new algorithms and data workflows.
• Establish best practices for reproducibility, versioning, testing, and deployment of scientific software.

Platform Development

• Build systems capable of handling large-scale multimodal scientific datasets, including:
  • Chemical compound libraries
  • Experimental assay readouts
  • -omics and imaging data
  • Biomedical literature and knowledge graphs

Qualifications

• Master's degree in fields such as: Computer Science, Software Engineering, Biomedical Engineering, Computational Chemistry, Bioinformatics, or related disciplines.
• Strong programming skills in languages such as Python, C++, JAVA, etc.
• Expertise in one or more scientific domains such as:
• Bioinformatics, cheminformatics, genomics, cellular or medical imaging.
• Experience building infrastructure for data science and ML workflows.
• Experience with scientific libraries (such as scikit-learn, Pandas, RDKit) deep learning frameworks (such as PyTorch, Tensorflow).
• Expertise in working with cloud platforms such as GCP and AWS.
• Experience working in drug discovery, biotechnology, or biomedical research environments.
• Experience managing large multimodal datasets in scientific contexts.