Computational Biologist

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🔥 2 hours ago

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

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Verge Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

🧬 Biotechnology

🤖 Artificial Intelligence

🏥 Healthcare

Biotechnology • Artificial Intelligence • Healthcare

Verge Labs is a biotech company applying frontier AI and large multimodal human brain datasets to build foundation models for precision neurology. Their platform uses proprietary multimodal patient brain data (deep molecular profiling, proteomic, genomic, clinical, and tissue samples) to train models that generate a 'virtual biopsy' of a patient's brain from routine blood draws. Products include vBx-1. 0, a foundation model predicting brain activity, therapy response, and side effects to improve target discovery, biomarker identification, patient stratification, and to reduce clinical trial size and risk. They partner with top pharmaceutical companies and aim to accelerate neuroscience drug development by surfacing targets and enabling more efficient clinical trials.

📋 Description

• Reporting to the Head of Product & Engineering, working alongside Verge's platform and computational biology teams • Define and enable new product offerings leveraging Verge’s drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers. • Develop cutting-edge computational methodologies integrating multi-omic datasets to develop predictive models for translational biology • Lead high-impact projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration • Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration

🎯 Requirements

• 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 biological domain knowledge with computational approaches to solve real scientific problems • Track record of individual innovation, with published research or shipped work influencing pharma R&D decisions • 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 experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.

🏖️ Benefits

• N/A