Research Officer (Large Language Models & Data Engineering), A*STAR BII

Job Summary

We are a project team comprising researchers from SingHealth, A*STAR, and Synapxe, developing a product to enhance the management and treatment of patients with lower respiratory tract infections (LRTIs)-a condition that accounts for nearly 489 million cases globally and is a major contributor to inappropriate antibiotic use in Singapore.

Over 40% of prescriptions for LRTIs are unnecessary, contributing to antimicrobial resistance (AMR)-a top global health threat. Traditional stewardship programs are expert-dependent and resource-intensive, limiting scalability.

To overcome these challenges, we are building an AI-powered Large Language Model platform that uses routinely collected clinical data to help clinicians identify cases that do not require antibiotics, thereby reducing unnecessary prescriptions.

Role:

As a Research Officer, you will play a central role by integrating Large Language Models (LLMs) for intelligent processing of unstructured clinical text-such as physician notes, discharge summaries, and radiology reports. Your work will help build explainable, scalable, and real-time AI recommendations that assist clinicians at the point of care for lower respiratory tract infections.

Key Responsibilities:

• Contribute to the LLM development, focusing on enhancing unstructured data processing for clinical and biomedical applications.
• Fine-tune and train LLMs (e.g., LLaMA, Mistral, Phi, and the GPT family) using supervised and instruction-based datasets.
• Design and implement pipelines for data cleaning, preprocessing, and tokenisation of large-scale text corpora.
• Integrate retrieval-augmented generation (RAG) and knowledge graph components for domain adaptation.
• Evaluate model performance using BLEU, ROUGE, BERTScore, and factual consistency metrics.
• Develop optimised PEFT/LoRA/QLoRA fine-tuning frameworks for efficiency on GPU clusters.
• Collaborate with researchers to design experiments, interpret results, and publish findings.
• Maintain reproducible codebases, documentation, and experiment logs.

Requirements:

• Master or Bachelor in computer science, data science, artificial intelligence, computational linguistics, or related disciplines.
• Strong experience in Natural Language Processing (NLP), transformer-based models, and text generation.
• Proficiency in Python, PyTorch, Hugging Face, Transformers, and LLM fine-tuning libraries (e.g., PEFT, DeepSpeed, bitsandbytes).
• Experience with text and data processing, including annotation, tokenisation, and augmentation.
• Familiarity with vector databases (FAISS, Qdrant) and RAG pipelines.
• Understanding of GPU-based training, distributed model optimisation, and experiment tracking (e.g., MLflow, W&B).
• Strong analytical, communication, and collaborative skills.

Preferred Experience

• Research experience with open-weight LLMs or domain-specific adaptation (e.g., biomedical).
• Experience with multi-agent frameworks, prompt engineering, or LLM safety evaluation.
• Familiarity with cloud computing (AWS, Azure) or on-prem GPU clusters.

What We Offer

• Opportunity to work on cutting-edge LLM research with measurable real-world impact.
• Access to high-performance GPU infrastructure and interdisciplinary collaborations.
• Mentorship and opportunities for publication, conference presentation, and project leadership.