Research Scientist Intern, AI Molecular Design

GenBio AI is an AI for Science company on a mission to make biology fully computable. We are building AIDO (AI-Driven Digital Organism) to simulate living systems, decode biology holistically, and accelerate medical and scientific research. Our first step toward that mission is AIDO Cell, the first virtual cell world model that holistically simulates cell biology from molecules up to whole-cell behavior. It enables researchers to run experiments virtually, predicting how cells respond to genetic changes, drugs and other interventions before going into the lab. It is supported by AIDO Foundry, an AI-builds-AI engineering framework for automating virtual cell development, and AIDO Lab, an interface to digitally run experiments and observe the results. Together they form the foundation for a new paradigm in AI-driven drug discovery, bioengineering, and fundamental biomedical research.

M.S. or Ph.D. student (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field. Available for a 6-month, in-person internship in Palo Alto, CA, and authorized to work in the U.S. for the duration of the internship. 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. Strong theoretical foundation (e.g., statistics, optimization, graph theory, linear algebra). Passion for interdisciplinary research (emphasizing the intersection of AI and Biology), and willingness to acquire necessary domain knowledge. Motivated and self-driven with the ability to operate with partial descriptions of high-level objectives (as is typical in a start-up environment). Familiarity with software engineering best practices (version control, documentation, etc).

3 year PhD student and above. 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. Intern experience in industry (e.g., OpenAI, FAIR, Deepmind, Google Research). Hands-on experience working at the intersection of AI and Biology, particularly protein structure prediction, protein sequence/structure modeling, or molecular design. Experience with biological structure prediction algorithms or models such as AlphaFold2/3 or RoseTTAFold. Experience in generative modeling for biological structures and sequences, including diffusion models, flow matching, or related approaches. Experience in large-scale distributed training and inference. Open-source contributions, especially if used by others.