Computational Biologist

Requirements

• Your Mission Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Computational Biologist (AI/ML) will be responsible for defining and enabling new product offerings leveraging Verge’s drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers.
• Your 12 Month Outcomes - Work with Verge’s AI partners to deliver a best-in-class biology foundation model with Verge's proprietary datasets - Develop a novel approach that enables a powerful new product offering (patient stratification, biomarker discovery, etc.) - Deliver at least two CONVERGE-powered insights projects to pharma/biotech companies - Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration You Will - Develop and evaluate cutting-edge
• Requirements Candidates must have: - Either: - PhD in computational biology, AI/ML, applied statistics, biophysics, or, - MS and professional
• experience in relevant fields. - ≥5 years of
• experience working in applied computational biology and integration of multi-omic datasets (RNA-seq, genotyping, clinical), with ≥2 years in a startup environment, - ≥2 years of
• experience in relevant areas of translational science, demonstrating a deep understanding of target identification, biomarker discovery, and/or patient stratification, - Proven ability to implement, evaluate, and/or create computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery, - Fluency with state of the art in systems biology workflows, including off-the-shelf biological databases and computational biology tools, - Track record of bridging
• Experience running a significant number of end-to-end RNA-Seq data analyses (from QC, read quantification, normalization through to interpretation), - Excellent coding skills in Python, with
• experience in relevant ML/AI libraries (e.g., PyTorch, HuggingFace, scikit-learn, pandas, numpy). A demonstrable portfolio (e.g., GitHub, research code, or shared notebooks) is highly preferred, -
• Experience in building and evaluating machine learning models on biological data, ideally with transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a deep understanding of feature selection, model interpretability, - Professional
• experience with AI workflows, including natural language processing (NLP), retrieval-augmented generation (RAG), embeddings, vectorization of diverse data types, and working with large language models (e.g., GPT), - Demonstrated