Scientist / Senior Scientist, Computational Biology

About the role

We 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.

Qualifications

A 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.

About Syenex Syenex is an open-science biotechnology company building the delivery technologies that unlock the future of genetic medicine. We engineer cell-specific delivery platforms—including enveloped vectors such as extracellular vesicles and viral vectors—that give cell and gene therapy developers best-in-class, plug-and-play tools spanning discovery through GMP manufacturing.

Founded in 2022 and based in Evanston and Chicago, Illinois, Syenex operates on an Open Science model: we make a continuously improving catalog of cell-engineering and delivery technologies universally accessible to academia and biopharma, freeing our partners to focus on their therapeutic insight. Through a growing network of partnerships and CDMO integrations, we help developers move from idea to clinical impact faster, at lower cost, and with higher delivery efficiency.

We are a small, multidisciplinary team that pairs rigorous synthetic biology with a build-fast, collaborate-openly culture. This role is central to how we scale that science.