San Francisco, California30+ days ago
li>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.
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.