Genbio
Research Scientist (AI) - Cell & Tissue Modeling
Genbio, Palo Alto, California, United States, 94306
Headquartered in Silicon Valley, we are a newly established start-up, where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of Generative AI. Our team comprises leading minds and innovators in AI and Biological Science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.
We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our exceptionally strong R&D team and leadership in LLM and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.
Key Responsibilities:
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. Nice to Have:
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:
Experience with cell-level data, particularly single-cell RNA-sequencing data. Experience with tissue-level data, particularly spatial transcriptomics, spatial proteomics, or microscopy (e.g. H&E, IF, IHC). Experience with methods development for aforementioned data types. Experience with multimodal or multiscale models (even in other domains, e.g. remote sensing, medical imaging). Deep knowledge of one or more of the following: variational autoencoders (especially biological variants like scVI), vision transformers, graph neural networks, neural fields, diffusion models, and self-supervised learning.
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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Key Responsibilities:
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. Nice to Have:
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:
Experience with cell-level data, particularly single-cell RNA-sequencing data. Experience with tissue-level data, particularly spatial transcriptomics, spatial proteomics, or microscopy (e.g. H&E, IF, IHC). Experience with methods development for aforementioned data types. Experience with multimodal or multiscale models (even in other domains, e.g. remote sensing, medical imaging). Deep knowledge of one or more of the following: variational autoencoders (especially biological variants like scVI), vision transformers, graph neural networks, neural fields, diffusion models, and self-supervised learning.
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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