Research Scientist, Applied Science

Responsibilities

• Combine research in AI for structural biology with the application of foundation models to real-world scientific challenges.
• Contribute to model development and lead computational discovery efforts in collaboration with external partners.
• Translate research advances into impactful outcomes in drug discovery and protein engineering.
• Split time between partner-facing scientific projects and internal research initiatives as needed.

Requirements

• PhD in Computer Science, Artificial Intelligence, Computational Biology, or a related technical field.
• Proven track record of research in top-tier AI/ML (e.g., NeurIPS, ICML, ICLR) or core biology (e.g., Nature, Science, Cell) journals and conferences.
• Experience translating machine learning research into real-world scientific impact.
• Experience in computational discovery for protein engineering, antibody discovery, or binder design.
• Experience working closely with experimental scientists to guide decisions using experimental results.
• Familiarity with software engineering best practices, including version control and documentation.
• Ability to operate effectively in a fast-paced startup environment with high-level objectives.

Preferred Qualifications

• 3+ years of post-PhD experience in an industry or postdoc role.
• Experience at a startup or top research industry labs (e.g., OpenAI, FAIR, DeepMind, Google Research).
• Experience with biological structure prediction algorithms such as AlphaFold2/3 or RosettaFold.
• Experience in generative modeling for biological structures and sequences.
• Deep knowledge of diffusion models, flow matching, and protein sequence models.
• Experience leading scientific collaborations or serving as a technical lead for external partners.

About the Company

GenBio AI develops multiscale foundation models to decode and simulate human biology. Our team is building AIDO (AI-Driven Digital Organism), a unified framework for predicting, simulating, and programming biology across all scales, starting with the engineering of the virtual cell.