Sobre este puesto de Research Scientist, Applied Science en Genbio
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.
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.
Job Requirements
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.
Preferred Qualifications
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
