{"id":1253622,"url":"https://alion.io/job/astar-research-officer-large-language-models-data-engineering-astar-bii","title":"Research Officer (Large Language Models & Data Engineering), A*STAR BII","company":{"id":50235,"name":"Agency for Science, Technology and Research","domain":"a-star.edu.sg","url":"https://alion.io/company/a-star-edu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"B","score":80,"open_postings":50,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-10-19T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":47000,"max_usd":116000,"period":"year","method":"role_country_seniority_unknown","sample_n":85},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bitsandbytes","optional":false},{"name":"DeepSpeed","optional":false},{"name":"FAISS","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Mistral","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"PEFT","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Transformers","optional":false},{"name":"Weights & Biases","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Prompt Engineering","optional":true}],"status":"live","first_seen_at":"2026-09-25T18:38:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-19T15:11:35Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"Job Summary\nWe 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.\nOver 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.\nTo 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.\nRole:\nAs 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.\nKey Responsibilities:\nContribute to the LLM development, focusing on enhancing unstructured data processing for clinical and biomedical applications.\nFine-tune and train LLMs (e.g., LLaMA, Mistral, Phi, and the GPT family) using supervised and instruction-based datasets.\nDesign and implement pipelines for data cleaning, preprocessing, and tokenisation of large-scale text corpora.\nIntegrate retrieval-augmented generation (RAG) and knowledge graph components for domain adaptation.\nEvaluate model performance using BLEU, ROUGE, BERTScore, and factual consistency metrics.\nDevelop optimised PEFT/LoRA/QLoRA fine-tuning frameworks for efficiency on GPU clusters.\nCollaborate with researchers to design experiments, interpret results, and publish findings.\nMaintain reproducible codebases, documentation, and experiment logs.\nRequirements:\nMaster or Bachelor in computer science, data science, artificial intelligence, computational linguistics, or related disciplines.\nStrong experience in Natural Language Processing (NLP), transformer-based models, and text generation.\nProficiency in Python, PyTorch, Hugging Face, Transformers, and LLM fine-tuning libraries (e.g., PEFT, DeepSpeed, bitsandbytes).\nExperience with text and data processing, including annotation, tokenisation, and augmentation.\nFamiliarity with vector databases (FAISS, Qdrant) and RAG pipelines.\nUnderstanding of GPU-based training, distributed model optimisation, and experiment tracking (e.g., MLflow, W&B).\nStrong analytical, communication, and collaborative skills.\nPreferred Experience\nResearch experience with open-weight LLMs or domain-specific adaptation (e.g., biomedical).\nExperience with multi-agent frameworks, prompt engineering, or LLM safety evaluation.\nFamiliarity with cloud computing (AWS, Azure) or on-prem GPU clusters.\nWhat We Offer\nOpportunity to work on cutting-edge LLM research with measurable real-world impact.\nAccess to high-performance GPU infrastructure and interdisciplinary collaborations.\nMentorship and opportunities for publication, conference presentation, and project leadership.","description_format":"text","description_chars":3306,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-25T18:38:00Z"}],"visa":[],"liveness":{"score":23,"band":"cold","label":"Long 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