Research Engineer, Centre for Frontier AI Research, IAIC

A*STAR Research Entities (A*STAR); Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

A*STAR Research Entities (A*STAR) is seeking a motivated Research Engineer to develop and implement AI methods that support drug discovery for RNA targets. The successful candidate will work with an interdisciplinary team to build computational tools for RNA structure modelling, molecular interaction prediction, and compound screening.

You will implement and optimise machine learning models, develop data processing pipelines for RNA sequences, structures, chemical compounds, and experimental

Qualifications

• Master's degree in Computer Science, Bioinformatics, Computational Biology, Computational Chemistry, or a related discipline.
• Strong Python programming skills and familiarity with software development practices, including version control, testing, and documentation.
• Hands-on experience with machine learning or deep learning frameworks such as PyTorch or TensorFlow.
• Experience processing scientific datasets and evaluating predictive models.
• Strong analytical and problem-solving skills, with the ability to work independently and collaboratively.
• Interest in applying AI to RNA biology and drug discovery.
• Publications in leading peer-reviewed journals or conferences in AI, computational biology, bioinformatics, or drug discovery, would be an advantage. Candidates with strong evidence of practical implementation through research projects, software contributions, or AI model development are also encouraged to apply.

Responsibilities

• Implement and optimise machine learning models for RNA structure prediction and RNA-small molecule interaction modelling.
• Develop data processing pipelines for RNA sequences, structures, chemical compounds, and experimental measurements.
• Train, fine-tune, and evaluate models using GPU computing resources.
• Support computational screening and prioritisation of compounds for experimental validation.
• Collaborate with researchers to integrate experimental results into computational workflows.
• Maintain reproducible code, technical documentation, and model evaluation pipelines.

Skills

Analytical skills

Collaboration

Independent work

Publications advantage

Education

Master's degree in CS/biocomputing

Tools

Python

PyTorch

TensorFlow

GPU computing

Job description

Job Summary

We are seeking a motivated Research Engineer to develop and implement AI methods that support drug discovery for RNA targets. The successful candidate will work with an interdisciplinary team to build computational tools for RNA structure modelling, molecular interaction prediction, and compound screening.

Key Responsibilties

• Implement and optimise machine learning models for RNA structure prediction and RNA-small molecule interaction modelling.
• Develop data processing pipelines for RNA sequences, structures, chemical compounds, and experimental measurements.
• Train, fine-tune, and evaluate models using GPU computing resources.
• Support computational screening and prioritisation of compounds for experimental validation.
• Collaborate with researchers to integrate experimental results into computational workflows.
• Maintain reproducible code, technical documentation, and model evaluation pipelines.

Job Requirements

• Master's degree in Computer Science, Bioinformatics, Computational Biology, Computational Chemistry, or a related discipline.
• Strong Python programming skills and familiarity with software development practices, including version control, testing, and documentation.
• Hands-on experience with machine learning or deep learning frameworks such as PyTorch or TensorFlow.
• Experience processing scientific datasets and evaluating predictive models.
• Strong analytical and problem-solving skills, with the ability to work independently and collaboratively.
• Interest in applying AI to RNA biology and drug discovery.
• Publications in leading peer-reviewed journals or conferences in AI, computational biology, bioinformatics, or drug discovery, such as Nature Communications, Nature Machine Intelligence, NeurIPS, ICML, and ICLR, would be an advantage. Candidates with strong evidence of practical implementation through research projects, software contributions, or AI model development are also encouraged to apply.

Desirable Experience

• Familiarity with RNA structure, cheminformatics, molecular modelling, or structure-based drug discovery.
• Experience with language models, graph neural networks, or other models for biological sequences and molecular structures.
• Familiarity with chemical probing data, such as SHAPE or DMS measurements.
• Experience with GPU computing, high-performance computing clusters, or distributed model training.