Computational Biologist

This is a fully remote position, open to applicants in California.

📋 Description

• Reporting to the Head of Product & Engineering, collaborating with Verge's platform and computational biology teams.

• Define and facilitate new product offerings that utilize Verge’s drug discovery engine for internal stakeholders, external partners (including both pharma and AI), and customers.

• Develop innovative computational methodologies that integrate multi-omic datasets to create predictive models for translational biology.

• Lead impactful projects that apply and adapt AI models to address translational challenges in disease biology, biomarker discovery, and target exploration.

• Construct an internal agentic AI workflow that enhances multi-modal biomedical reasoning and orchestration.

⛳️ Requirements

• Either:

• PhD in computational biology, AI/ML, applied statistics, biophysics, or related fields, or

• MS and relevant professional experience.

• A minimum of 5 years of experience in applied computational biology and the integration of multi-omic datasets (RNA-seq, genotyping, clinical), with at least 2 years in a startup environment.

• At least 2 years of experience in pertinent areas of translational science, showcasing a deep understanding of target identification, biomarker discovery, and/or patient stratification.

• Demonstrated ability to implement, evaluate, and/or develop computational methodologies that utilize machine learning, statistics, and AI for biological research and discovery.

• Proficiency with cutting-edge systems biology workflows, including established biological databases and computational biology tools.

• History of connecting biological domain knowledge with computational strategies to address real scientific challenges.

• Proven track record of individual innovation, with published research or contributions that have influenced pharmaceutical R&D decisions.

• Experience conducting a substantial number of end-to-end RNA-Seq data analyses (from quality control, read quantification, normalization to interpretation).

• Strong 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 assessing machine learning models on biological data, ideally utilizing transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a thorough understanding of feature selection and 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 experience with model evaluation and experimental design in a scientific context, including the establishment of appropriate benchmarks and controls.

🏝️ Benefits

• N/A