Computational Scientist - Agentic AI

About Us

We are building an AI-native target discovery platform that blends multi-omics biology, graph intelligence, and agentic AI to find high-confidence therapeutic targets with mechanistic clarity. Our mission is to reduce clinical trial failures by deeply understanding disease biology, mechanistic pathways, and patient heterogeneity at the molecular level. We work across oncology, neurodegeneration, immunology, rare & complex diseases, building explainable and testable target hypotheses.
Position SummaryWe are seeking a Computational Scientist specializing in Agentic AI, multi-omics integration, and target discovery. The successful candidate will build AI agents that autonomously perform hypothesis generation, omics interpretation, graph-based inference, literature reasoning, and prioritization of candidate drug targets.This role sits at the interface of computational biology, AI, systems biology, and translational science, working closely with biological scientists, data engineers, and drug discovery teams.• Develop agentic AI systems that autonomously reason, interpret data, read literature, and propose testable biological hypotheses.  • Build graph-based biological networks (genedisease–pathway–interaction) to discover novel target mechanisms.  • Apply GNNs, network medicine, Node2Vec, and embeddings to predict disease–gene associations and pathway relevance.  • Design scoring frameworks for target essentiality, druggability, safety, and clinical translation.  • Integrate transcriptomics, scRNA-seq, proteomics, epigenetics (ATAC-seq, methylation), and CRISPR screens.  • Extract mechanistic signatures, pathway dysregulation, and cell-type dependencies.  • Build harmonized pipelines for multi-omics interpretation and target prioritization.  • Develop LLM-based reasoning agents to extract mechanisms, biomarkers, and targets from literature and clinical evidence.  • Build domain-aware knowledge assistants for interactive hypothesis generation and validation.  • Build scalable, reproducible omics data pipelines using cloud/HPC environments.  • Create interactive target knowledge dashboards integrating omics, pathways, literature, and AI insights.  • Collaborate with wet-lab teams for in-vitro / in-vivo validation of AI-derived hypotheses.  • Work with biologists, clinicians, and drug discovery teams to refine computational hypotheses into biological studies.  • Contribute to high-impact publications, patents, and grant proposals.  • Engage with industry partners, biotech startups, and translational research teams to advance AI-driven target discovery.QUALIFICATIONS & PREFERRED EXPERIENCE• PhD or equivalent experience in Computational Biology, Bioinformatics, AI/ML, or related field.  • Hands-on experience with omics data (at least RNA-seq or scRNA-seq) and basic biological interpretation.  • Strong programming skills in Python (R is a plus).  • Familiarity with machine learning workflows — model training, evaluation, and interpretation.  • Interest or exposure to graph-based methods (GNNs, network biology) OR willingness to learn.  • Experience working with real biological datasets or clinical cohorts.  • Comfortable with Linux environments and reproducible research practices.  • Ability to communicate biological insights clearly through reports or presentations.  • Curiosity-driven mindset and openness to work with LLMs/agentic AI systems.