Scientist / Senior Scientist, Computational Biology in Evanston, Illinois at Syenex

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.

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.