Longevity Researcher

Insilico Medicine

We are looking for people to help us develop research-grade AI systems and train LLMs with a solid grasp of the biological ground truth. Your primary task will be developing the pipeline that would inject biological knowledge into LLMs (https://insilico.com/MMAI). More specifically, you will be responsible for teaching LLMs how to reason about aging and how to discover novel anti-aging gene targets and geroprotectors.

This role requires both biological and programming background, especially in ML and AI fields. The position requires writing and submitting reviews and original research papers to Q1 journals. Other routine activities include dataset scouting and preprocessing, building PoC AI applications, systematic reviews of the current state of the LLM and aging fields, critical review of literature and methodologies.

Responsibilities:

• Identify, evaluate, and curate biological and omics datasets from public and proprietary sources
• Perform database review, annotation, harmonization, and quality control to support downstream analytical and AI workflows
• Design, develop, and validate proof-of-concept AI agentic systems for biological and biomedical research tasks
• Apply advanced bioinformatics, statistical, and machine-learning methods to extract actionable insights from complex biological data
• Author high-quality academic and scientific texts, including research manuscripts, reports, and technical documentation
• Collaborate closely with cross-functional research and engineering teams to translate scientific questions into scalable computational solutions

General Requirements:

I. Education

Master s/PhD degree in bioinformatics, CS, or biology.

II. Experience and Skills

• 6+ years in Python (async/aiohttp, ML & stats libraries, transformers, data visualization)
• 2+ years in R, including omics-related Bioconductor packages
• 5+ years in omics & bioinformatics research, from raw data to conclusions (DEG, GSEA, survival analysis; KEGG, GO, GEO, UniProt, etc.)
• Strong knowledge of omics platforms (DNAm, transcriptomics, proteomics, single-cell)
• Research excellence: hypothesis-driven work, experimental design, advanced statistics, independent academic writing
• Aging biology expertise: aging clocks, hallmarks of aging, geroprotectors, animal models
• Proven experience with AI/ML in biology (AI agents, fine-tuned models, or public datasets/models)
• Strong AI literacy (LLMs via API, deep learning fundamentals, AI risks awareness)
• Academic publishing track record ( 5 publications/preprints; 2 in aging/longevity)
• C1 English (academic writing, clear communication)
• Ability to work independently, under pressure, and align research with business priorities