Computational Biologist

Department: Computational Biology

Location: San Francisco - Remote

Employment Type: FullTime

Who We Are

Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field’s largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.

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 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,

• Lead partnerships with AI companies to co-develop next-generation foundation models for drug discovery

• Frame biological problems in computational terms and design solutions that are biologically meaningful, interpretable, and experimentally testable,

• Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications,

• Translate between biological domain knowledge and machine learning objectives.

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 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.

Finally, we seek candidates who embrace our values and way of working:

• Ability to thrive in uncertainty with frequently changing priorities

• Deep alignment with our values

• A passion for making an impact on patients