AI Research Scientist, Biological Foundation Models

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.

The successful applicant will work as part of a small, close-knit team at the hub of Quiver’s scientific programs. This position will actively mentor team members and set technical direction alongside contributing production-quality code and models 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 strong computer scientist with deep expertise in representation learning, foundation model architectures, and multi-modal data alignment, with a demonstrated track record of leading teams while remaining deeply hands-on in coding, experimentation and system development, 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.

Responsibilities and Duties

Design, lead and directly implement computational pipelines for feature engineering and data integration for scientific data analysis.

Lead collaboration efforts while actively contributing to system architecture and implementation with internal and external teams responsible for production AI systems (e.g., LLM-based tools, agentic workflows). Provide input on system design, identify opportunities for new capabilities, and help define and integrate modeling workflows into user-facing applications.

Develop machine learning models to learn and align shared representations across heterogeneous biological datasets (e.g., time-series electrophysiology data, imaging, and perturbation assays).

Build and maintain scalable systems for embedding generation, storage, and retrieval (e.g., vector-based search, nearest-neighbor lookup) to support downstream analysis and querying. Own and develop rigorous evaluation frameworks and benchmarks for cross-modal embedding quality, including alignment metrics, retrieval precision, and biological plausibility checks against known drug-target and gene-pathway relationships. Partner closely with cross-functional wet-lab teams to design active learning cycles where model predictions suggest and prioritize validation experiments.

Lead by example in the development and deployment of AI/ML systems, ensuring best practices in reproducibility, version control, performance optimization, and documentation within a modern cloud-based environment.

Contribute to the development of data and machine learning pipelines, including implementation, evaluation, optimization, and maintenance of models and associated workflows. Balance team leadership with individual technical contributions, 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.