Verge Genomics

Computational Biologist

San Francisco - Remote

Sponsorship not specifiedDetected 245 days ago

PythonGitMachine LearningPyTorchscikit-learnPandasNumPyNLPLLMsRAGAgentic AIStatisticsBioinformaticsResearchExperimental Design

About the role

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.

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
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,

Candidates must have:
Demonstrated experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.
Ability to thrive in uncertainty with frequently changing priorities

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,
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),

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
Deep alignment with our values
A passion for making an impact on patients
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.

This listing is sourced directly from Verge Genomics's careers page and normalized into a canonical job model.