Research Scientist
il y a 2 semaines
Our Science team is a talented group of interdisciplinary researchers focused on the extension, use, and integration of biological foundation models to solve the most important biomedical and biological problems. This is a unique opportunity to shape the direction of our applied research, contribute to groundbreaking discoveries, and accelerate real-world applications. Responsibilities
- Lead and contribute to the development, adaptation, and application of one or more of the following biological foundation models: sequence, structure, single cell, imaging, phenotype and more. Lead and contribute to the development, adaptation, and application of integrated biological foundation models that bridge modalities and scales
- Collaborate with internal and external research partners to extend model capabilities and translate cutting-edge methods into applied biological insights.
- Design and execute experiments leveraging pan-modal biological data, from single-cell and spatial transcriptomics to multi-omic datasets.
- Identify high-impact opportunities for model-driven discoveries, validation studies, and new biological use cases.
- Mentor junior researchers and contribute to a culture of scientific excellence and innovation.
- Communicate research outcomes in internal/external reports, presentations, and top-tier journals/conferences.
- MSc / PhD (or equivalent expertise) in Computational Biology, Machine Learning, Bioinformatics, or a related technical field.
- Proven track record in research and innovation in the AI for biology space as demonstrated through publications in top-tier AI/ML (e.g., NeurIPS, ICML, ICLR) and/or core biology (e.g., Nature, Science, Cell) venues.
- Experience developing and debugging deep learning models in PyTorch, JAX, or TensorFlow, ideally with an emphasis on generative models, graph neural networks, or large-scale biological data applications.
- Strong understanding of biological data modalities, including some of the following: Single-cell RNA-seq, Single cell epigenetic data, Structure data analysis, Analysis of biomedical imaging data, Human phenotype data or other modalities that are relevant to the human simulation vision.
- Passion for interdisciplinary research at the intersection of AI and Biology, and willingness to acquire domain-specific expertise.
- Motivated and self-directed, capable of operating in a fast-paced, startup environment.
- Familiarity with software engineering best practices (version control, documentation, testing) and a record of open-source contributions preferred.
- 3+ years of postdoctoral or industry experience applying ML to biological data.
- Experience with genomics, transcriptomics, or proteomics datasets and functional assays (e.g., ATAC, Hi-C, CAGE).
- Familiarity with multi-omics, health, or EHR datasets and public data repositories (NCBI, ENCODE, ENSEMBL, TCGA, UK Biobank).
- Experience integrating and curating biological datasets for model training or hypothesis generation.
- Knowledge of multimodal or multiscale models, including transformers, diffusion models, VAEs, or graph neural networks.
- Prior exposure to large-scale distributed training and inference environments, and ML on accelerators.
- Background in network or systems biology, including gene regulatory networks, clustering, and embedding algorithms.
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. GenBio AI participates in the U.S. Department of Homeland Security's E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e- We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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