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Verge Genomics · 1 month ago

Computational Biologist (AI/ML)

United States

Full-time

Remote

Senior Level

5+ years exp

Verge Genomics is focused on accelerating drug development through AI and human data. The Computational Biologist (AI/ML) will work with internal teams and external partners to develop innovative product offerings and insights projects leveraging the company's proprietary drug discovery engine.

BiotechnologyGeneticsHealth CareMachine LearningNeuroscience

Responsibilities

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

Qualification

Computational BiologyMachine LearningMulti-Omic Data IntegrationAI MethodologiesPythonBiomarker DiscoveryTranslational ScienceStatistical AnalysisCollaborationProblem SolvingAdaptability

Required

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