Research Scientist, Applied Science
TLDR
Lead AI-driven structural biology research and computational discovery, translating advances into real-world outcomes via external collaborations.
PhD (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Computational Biology, 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.
Experience in translating machine learning research into real-world scientific impact through collaborations with academic, biotechnology, or pharmaceutical partners.
Experience designing, executing, or supporting computational discovery campaigns in protein engineering, antibody discovery, binder design, or related therapeutic discovery efforts.
Experience working closely with experimental scientists and using experimental results to guide decisions.
Prior experience working on AI for structural biology or drug discovery in either an academic or industry setting.
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.
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).
Experience in biological structure prediction algorithms such as Alphafold2 & 3, RosettaFold.
Experience in generative modeling for biological structures and sequences
Experience leading scientific collaborations or serving as a technical point of contact for external research partners.
Deep knowledge of diffusion models, flow matching, and protein sequence models
GenBio AI develops multiscale foundation models to decode and simulate human biology, specifically by engineering the virtual cell to model the fundamental unit of life. Our platform accelerates advancements in drug discovery and healthcare by utilizing AI-driven digital organisms to predict and program biological systems on all scales.