About the roleWe are seeking a Scientist / Senior Scientist in Computational Biology to build the computational backbone of our R&D engine. Operating at the intersection of computational and experimental biology, you will lead high-throughput, library-based discovery campaigns, integrate machine learning and AI into our biomolecular design efforts, and build the automation and internal software that accelerate our research and operations. This is a hands-on, cross-functional role for someone who is equally comfortable writing production-quality code, applying modern ML methods to protein, DNA, and RNA engineering, and partnering closely with bench scientists to turn data into better molecules.
What you'll do- Work directly with our Co-founder and CSO on dedicated projects.
- Lead the computational aspects of method development, including high-throughput, library-based discovery and refinement campaigns-for example, binder protein discovery and optimization.
- Integrate machine learning and AI methods into our protein, DNA, and RNA engineering, iteration, and product-development workflows, including computational prediction of biomolecular structures.
- Collaborate with the R&D team to design and deliver customized automation, data analysis, data visualization, and biomolecular design tools, and partner directly with bench scientists to produce high-quality, analysis-ready data and feed results back fast enough to inform the next round of experiments.
- Build and maintain custom internal software packages for managing research and operations-spanning automated workflows, DNA and library design, and data analysis.
- Advise on best practices for the development, use, and validation of machine learning tools across R&D.
- Facilitate the thoughtful adoption and deployment of enterprise AI tools throughout operations, using them heavily in your own work-for experimental planning, tool development, and data interpretation.
QualificationsA competitive candidate will bring a combination of training and experience that enables them to lead collaborative work of the type described above. Exemplary credentials include:
- A MS or PhD in a relevant scientific, engineering, and/or data science discipline.
- Dedicated training/experience in contemporary machine learning methodology, best practices, and applications.
- At least 3 years of relevant industry experience, including high-throughput biology research, machine learning for R&D, and automation of data and design workflows.
- Facility with enterprise AI tools, including but not limited to Claude Code, as well as computational biomolecular design packages employing AI/ML.
- Specific experience in biotechnology.
- Ability to thrive in a dynamic, collaborative research environment while balancing scientific rigor with rapid iteration.
- Demonstrated curiosity, creativity, and ownership in driving projects forward.
- Strong problem-solving skills and ability to navigate ambiguity in a fast-paced research environment.
- Ability to work independently while collaborating effectively across computational and bench science teams.
