GenBio AI -
Company: GenBio AI –
WebSite: Dubai, United Arab Emirates
Job Description:**Option: Formal**
Genbio. AI, Inc. (hereinafter referred to as “GenBio AI”) is an innovative, globally-oriented startup organization dedicated to the advancement of foundation models (FMs) within the field of biology. The company’s objective is to develop transformative FMs through the application of pan-modal biological data analysis at all levels of biological organization. The overarching goal is to achieve a comprehensive and empirically-grounded understanding of the mechanisms governing organismal physiology and disease. It is anticipated that this endeavor will foster a paradigm shift in drug design, bio-engineering, personalized medicine, and fundamental biomedical research, all predicated upon the principles of Generative Biology.
The founding team comprises internationally recognized scientists and researchers in the fields of Artificial Intelligence and Biology, representing prominent institutions such as Carnegie Mellon University and Stanford University, alongside established financial investors. The management team possesses substantial technical and managerial expertise, with backgrounds encompassing leading academic institutions in the United States and France, including Carnegie Mellon University, École Normale Supérieure, École Polytechnique, and Inria, as well as prominent technology companies such as Isomorphic Labs and Meta. Furthermore, the advisory board includes Nobel Laureates in Chemistry, Medicine, and Economics, Turing Award Laureates, and senior policymakers from the United States and the United Kingdom.
GenBio AI, a globally-focused organization from its inception, is establishing offices in Palo Alto, Paris, and Abu Dhabi.
**Job Requirements:**
* A doctoral degree (Ph.D.) or demonstrable equivalent expertise in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline is required.
* A proven record of research and innovation, as evidenced by publications in leading AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, Cell) journals and conferences.
* Proficiency in the development, implementation, and debugging of deep learning methods and models within prevalent frameworks such as JAX, TensorFlow, or PyTorch, with a demonstrated interest in generative models, graph neural networks, or large-scale deep learning applications.
* A strong theoretical foundation in statistics, optimization, graph algorithms, and linear algebra, coupled with experience in building models from first principles.
* A demonstrable passion for interdisciplinary research, with a specific emphasis on the intersection of Artificial Intelligence and Biology, and a willingness to acquire necessary domain-specific knowledge.
* Motivation and self-direction, with the capacity to operate effectively in an environment characterized by partial and incomplete descriptions of high-level objectives, as is typical in a startup setting.
* Documented familiarity with and utilization of software engineering best practices (version control, documentation, etc.), and contributions to open-source projects, particularly those with demonstrated adoption.
**Qualifications:**
* A minimum of three (3) years of post-doctoral experience in an industry or post-doctoral research role.
* Prior experience within a startup environment or leading research laboratories (e.g., OpenAI, FAIR, DeepMind, Google Research).
* Practical experience at the intersection of AI and Biology.
* Experience in large-scale distributed training and inference, and Machine Learning on specialized hardware accelerators.
**Preferred Qualifications:**
* Prior experience working with diverse biological datasets, including but not limited to bulk/single-cell transcriptomics (e.g., RNA-Seq), epigenetics (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 transcription factor-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).
* Practical 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.
We invite qualified candidates to join our organization as we pursue the redefinition of the future of biology and medicine.
GenBio AI is an equal opportunity employer. The company embraces diversity and is committed to fostering an inclusive environment for all employees.
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