Genbio
Research Scientist (AI) - Interactome
Genbio, Palo Alto, California, United States, 94306
Genbio. AI, Inc. (GenBio AI) is an innovative global startup dedicated to developing foundation models (FM) for biology.
We aim to train transformative FMs on pan-modal biological data at all levels. Our goal is to achieve comprehensive, actionable empirical understandings of the mechanisms underlying all organismal physiologies and diseases. This will pave the way for a new paradigm in drug design, bio-engineering, personalized medicine, and fundamental biomedical research, all powered by Generative Biology.
Our founding team consists of world-renowned scientists and researchers in AI and Biology from prestigious institutions such as CMU and Stanford, alongside prominent financial investors.
Our management team boasts strong technical and managerial backgrounds, hailing from top academic institutions in the US and France, including CMU, ENS, L’X, and Inria, as well as leading tech companies like Isomorphic and Meta. Additionally, our advisory board features Nobel Prize Laureates in Chemistry, Medicine, and Economics, Turing Award Laureates, and senior policymakers from the US and UK.
GenBio AI a true global effort from day one, is establishing offices in Palo Alto, Paris, and Abu Dhabi.
Job Requirements:
PhD (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field. Proven track record in research and innovation demonstrated through contributions in top-tier AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, or Cell) journals and conferences. Skilled in developing, implementing, and debugging deep learning methods/models in popular frameworks, such as JAX, TensorFlow, or PyTorch, with an interest in generative models, graph neural networks, or large-scale deep learning applications. A strong theoretical foundation (statistics, optimization, graph algorithms, linear algebra) with experience building models ground up. A passion for interdisciplinary research (with an emphasis on the intersection of AI and Biology), and willingness to acquire necessary domain knowledge. Motivated and self-driven with the ability to operate with partial and incomplete descriptions of high-level objectives (as is typical in a start-up environment). Evidence of familiarity and utilization of software engineering best practices (version controlling, documentation, etc), and open-source contributions, especially if used by others. Qualifications:
3+ years of post-PhD experience in an industry or postdoc role Prior experience working at either a start-up or top research industry labs (e.g., OpenAI, FAIR, Deepmind, Google Research). Hands-on prior experience working at the intersection of AI and Biology. Experience in large-scale distributed training and inference, ML on accelerators. Preferred Qualifications
Prior experience working with diverse biological datasets, including but not limited to bulk/single transcriptomics (e.g., RNA-Seq), epigenetic (e.g., ATAC/ChIP-Seq), proteomics/phosphoproteomics (e.g., mass-spec), and genetics (e.g., GWAS) datasets. Familiarity with diverse biological networks, including but not limited to protein-protein interaction, gene-gene expression, and TF-Target Gene regulatory networks. Prior experience developing algorithms for network/systems biology (e.g., network construction/inference, clustering, embedding, etc) Familiarity with Graph ML frameworks, such as Pytorch Geometric, Deep Graph Library (DGL), and Nvidia RAPIDS (cuGraph/cuML). Hands-on experience with geometric deep learning models such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Familiarity with traditional (e.g., TransE, RotatE, etc.) and deep (ULTRA) representation learning algorithms for large knowledge graphs.
Join us as we embark on this journey to redefine the future of biology and medicine. We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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Job Requirements:
PhD (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field. Proven track record in research and innovation demonstrated through contributions in top-tier AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, or Cell) journals and conferences. Skilled in developing, implementing, and debugging deep learning methods/models in popular frameworks, such as JAX, TensorFlow, or PyTorch, with an interest in generative models, graph neural networks, or large-scale deep learning applications. A strong theoretical foundation (statistics, optimization, graph algorithms, linear algebra) with experience building models ground up. A passion for interdisciplinary research (with an emphasis on the intersection of AI and Biology), and willingness to acquire necessary domain knowledge. Motivated and self-driven with the ability to operate with partial and incomplete descriptions of high-level objectives (as is typical in a start-up environment). Evidence of familiarity and utilization of software engineering best practices (version controlling, documentation, etc), and open-source contributions, especially if used by others. Qualifications:
3+ years of post-PhD experience in an industry or postdoc role Prior experience working at either a start-up or top research industry labs (e.g., OpenAI, FAIR, Deepmind, Google Research). Hands-on prior experience working at the intersection of AI and Biology. Experience in large-scale distributed training and inference, ML on accelerators. Preferred Qualifications
Prior experience working with diverse biological datasets, including but not limited to bulk/single transcriptomics (e.g., RNA-Seq), epigenetic (e.g., ATAC/ChIP-Seq), proteomics/phosphoproteomics (e.g., mass-spec), and genetics (e.g., GWAS) datasets. Familiarity with diverse biological networks, including but not limited to protein-protein interaction, gene-gene expression, and TF-Target Gene regulatory networks. Prior experience developing algorithms for network/systems biology (e.g., network construction/inference, clustering, embedding, etc) Familiarity with Graph ML frameworks, such as Pytorch Geometric, Deep Graph Library (DGL), and Nvidia RAPIDS (cuGraph/cuML). Hands-on experience with geometric deep learning models such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Familiarity with traditional (e.g., TransE, RotatE, etc.) and deep (ULTRA) representation learning algorithms for large knowledge graphs.
Join us as we embark on this journey to redefine the future of biology and medicine. We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
#J-18808-Ljbffr