Position - Biomedical AI Scientist - Quiver Bioscience Inc - Cambridge, MA

Title of Position:
Biomedical AI Scientist Summary of Position

This AI Scientist will work on computational projects related to Quiver’s drug discovery platform, including helping architect the next generation of our drug discovery intelligence platform. While this role involves foundational work in signal and image processing for our proprietary optical electrophysiology data, there will be a strong focus in expanding this data into a multimodal reasoning engine. The candidate will work on projects integrating complementary data streams (e.g., transcriptomics, high‑content imaging, clinical datasets, etc.)

using knowledge graphs, generative AI, and retrieval‑augmented (RAG) methods to answer questions related to human disease and drug discovery. The successful applicant will work as part of a small, close‑knit team at the hub of Quiver’s scientific programs to derive actionable insights from a broad range of relevant biomedical data modalities, including the company’s proprietary all‑optical electrophysiology data and other genomics/omics and imaging data sets.

The ideal candidate will be a thoughtful and creative programmer with expertise in artificial intelligence and machine learning and a passion for applying technology to healthcare‑related problems.

Quiver Bioscience is a technology‑driven company established to create transformational medicines for the brain. We combine proprietary single‑cell functional assays with other multi‑modal measurements to discover new biology and new drug targets. We take advantage of cutting‑edge AI/ML to build the world’s most information‑rich maps of neuronal function to drive our drug discovery programs.

This position is based in Cambridge, MA, with the expectation of on‑site presence 3‑4 days per week to support lab integration, team meetings, and collaborative project work. Fully remote applicants will not be considered.

• Build computational pipelines for feature engineering and data integration for scientific data analysis.
• Design and build hybrid agentic AI workflows that fuse proprietary optical electrophysiology (oEP) measurements with semantic knowledge from literature and databases.
• Implement and fine‑tune joint embedding models (using techniques like contrastive learning and transformers) to align physiological signals with biological concepts (genes, pathways, and phenotypes).
• Construct and manage vector database and knowledge graph infrastructure required to store, index, and query high‑dimensional biological signatures for rapid similarity search.
• Apply deep learning (e.g., 1D‑CNNs, temporal encoders) to feature‑engineer “fingerprints” from high‑throughput voltage imaging data, serving as the empirical foundation of the platform.
• Collaborate with wet‑lab biologists to design active learning cycles where model predictions automatically suggest and optimize validation protocols (e.g., CRISPR or drug screening).
• Lead the software development lifecycle for AI products, ensuring rigorous standards for reproducibility, source control, performance optimization, and documentation in a Python/PyTorch/Cloud environment.
• Contribute to all aspects of analytics software development including design, implementation, source control, performance optimization, unit testing, defect management, documentation, and ongoing maintenance and support.
• Proficiently manage timelines, relationships, and work priorities to comfortably operate independently to make an impact.
• Utilize excellent interpersonal skills to build consensus, share insights with relevant stakeholders, deliver interpretable data products, and serve both business and scientific goals of the company with your work.

• PhD degree or corresponding demonstrable professional experience in Computer Science, Artificial Intelligence, Neuroscience, Physics, Computer Vision, Electrical Engineering, Mathematics, or related technical discipline (e.g., engineering, science, or biology with a strong quantitative flavor).
• Demonstrated experience building systems with Large Language Models (LLMs), including designing retrieval‑augmented generation (RAG) workflows,…