• 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 afore-mentioned 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. 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-verify.gov.

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.