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Culture Statement

At Quiver Bioscience, we are united by a shared purpose to transform the lives of patients with devastating CNS conditions. We advance with urgency and integrity, grounded in rigorous, data-driven science and a deep commitment to transparency and patient-centered impact. We are inventors that value the role that technology can play in disrupting difficult areas. Collaboration across disciplines and sectors fuels our innovation, while our scalable discovery platform empowers precision and personalization across CNS indications and drug modalities. Together, we strive for excellence guided by responsibility to deliver therapeutics that are better, safer, and more effective.

“PROGRESS IN SCIENCE DEPENDS ON NEW TECHNIQUES, NEW DISCOVERIES, AND NEW IDEAS – PROBABLY IN THAT ORDER.” – SYDNEY BRENNER

“We do well, by doing good.”

Every Quiver team member plays a vital role in our mission to transform CNS therapeutics discovery.

Our team is fueled by ambition, driven by collaboration, and defined by resilience. If you strive to be inspired and contribute to the important work we achieve, then you may be a great fit for our organization!

Quiver Bioscience offers competitive financial compensation and a comprehensive benefits program aimed at improving the financial, physical, and personal health and well-being of our employees and their families.

Current opportunities

AI Research Scientist, Biological Foundation Models 

Biology

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This AI Research Scientist will lead the design and build biological foundation models that learn shared representations across Quiver’s large-scale all-optical electrophysiology and multi-omic datasets. The role also involves hands-on development of signal and image processing for our proprietary optical electrophysiology data. The candidate will both lead and directly implement projects that integrate complementary data streams (e.g., transcriptomics, high-content imaging, clinical datasets, etc.) using deep representation learning, contrastive objectives, transformer architectures, and multi-modal fusion to build predictive models of disease biology and drug mechanism.

Cambridge, MA

Full-Time

Job Title

Department

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