Computational Biologist

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🕒 Julho 28

🏄 California – Remoto

🏄 California – Remote

⏰ Tempo Integral

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Verge Labs

SiteLinkedInTodas as Vagas

11 - 50 funcionários

🧬 Biotecnologia

🤖 Inteligência Artificial

🏥 Saúde

Biotechnology • Artificial Intelligence • Healthcare

A Verge Labs é uma empresa de biotecnologia que aplica IA de ponta e grandes conjuntos de dados multimodais do cérebro humano para construir modelos base para a neurologia de precisão. Sua plataforma utiliza dados cerebrais multimodais proprietários de pacientes (perfilagem molecular profunda, proteômica, genômica, amostras clínicas e de tecidos) para treinar modelos que geram uma "biópsia virtual" do cérebro de um paciente a partir de coletas de sangue de rotina. Os produtos incluem o vBx-1. 0, um modelo base que prevê atividade cerebral, resposta a terapias e efeitos colaterais para melhorar a descoberta de alvos, identificação de biomarcadores, estratificação de pacientes e reduzir o tamanho e o risco de ensaios clínicos. Eles fazem parcerias com as principais empresas farmacêuticas e visam acelerar o desenvolvimento de medicamentos em neurociências ao revelar alvos e possibilitar ensaios clínicos mais eficientes.

Descrição

• 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

🎯 Requisitos

• 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.

🏖️ Benefícios

• N/A