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Palo Alto, California, United States
Onsite
GenBio AI develops multiscale foundation models to decode and simulate human biology. Our team is accelerating towards an ambitious future where scientists can unlock humanity's biggest challenges in drug discovery, healthcare, and fundamental research with AIDO (AI-Driven Digital Organism): a unified framework for predicting, simulating, and programming biology across all scales. The foundation of this vision begins today as we engineer the virtual cell to model and simulate the fundamental unit of life.
This vision has brought together a talent-dense group of product-minded researchers and engineers dedicated to bringing it to reality. Our team prides itself on our strong engineering culture and highly interdisciplinary and collaborative approach. We are based in Palo Alto, with satellite offices in Paris and Abu Dhabi.
This role combines research in AI for structural biology with the application of our models to real-world scientific challenges. The successful candidate will contribute to model development, lead computational discovery efforts in collaboration with external partners, and help translate research advances into impactful outcomes. Depending on business needs, time may be split between partner-facing scientific projects and internal research initiatives.
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