Deep Learning Scientist

Overview

Talent Acquisition Partner at Connecticut Innovations | VC | TA & People Consulting for Startups

Are you ready to join Connecticut Innovation’s vibrant community of innovators? Connecticut Innovations ("CI") is Connecticut’s strategic venture capital arm, and we are passionate about serving our portfolio of 220+ companies across various industries, with strengths in life sciences, technology, and climate tech.

Come join Bexorg, Decode the Brain. Reinvent Drug Discovery!

Recent funding news:
Bexorg Raises $42.5M to Transform CNS Drug Development with World’s First Integrated AI and Whole-Human Brain Platform

About Bexorg

Bexorg develops systems that plan and execute human brain perfusion experiments and manage a growing biobank (10,000+ samples and growing rapidly) with zero tolerance for data failures.

Since Bexorg was founded by Dr. Zvonimir Vrselja and Dr. Nenad Sestan at Yale University in 2021, the company has rapidly advanced its whole-human brain drug discovery platform. The company’s foundational research, which demonstrated that cellular and metabolic activity could be restored to postmortem brains hours after death, was published in Nature (Nature 568, 336–343 (2019)). In 2024, Bexorg partnered with the University of Oxford and the UK Medical Research Council as part of a collaborative effort to advance translational gene therapy development for neurological diseases.

In mid-2025, the company announced a research collaboration with pharmaceutical company Biohaven Ltd. to advance two of Biohaven’s preclinical development programs. In the last 18 months, Bexorg has expanded its proprietary dataset to include hundreds of whole-brain experiments spanning Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative conditions, establishing one of the world’s largest repositories of human CNS data.

Building on continuous growth of its proprietary human brain dataset, Bexorg is now training foundation models on petabyte-scale human brain molecular data, creating the first AI system grounded in experimentally measured human neurobiology.

Role Summary - Deep Learning Scientist

Model Development: Design, develop, and deploy state-of-the-art deep learning models for analyzing multi-modal biological data.

Integration of Biological Data: Collaborate with bioinformatics, experimental biology and engineering teams to integrate multi-modal human biology datasets into cohesive AI frameworks.

Innovative AI Architectures: Develop deep learning architectures that incorporate biological inductive biases, and explore generative graph representation learning to

Scalability & Optimization: Optimize deep learning pipelines for petabyte-scale datasets and ensure models are scalable on high-performance computing infrastructures.

Validation & Iteration: Rigorously validate model outputs against biological benchmarks and iterate based on experimental feedback.

Scientific Communication: Publish research findings and present at scientific conferences to contribute to the broader AI and biomedical communities.

Stay Current: Keep abreast of the latest advancements in deep learning and AI, ensuring our models leverage cutting-edge innovations.

Qualifications

• PhD in Computer Science, Machine Learning, or alternative STEM field (e.g., biology or physics) with appropriate experience
• Demonstrated track record of applying deep learning to biological problems (e.g., genomics, transcriptomics, proteomics, or imaging).
• Graph & Geometric Deep Learning:
  Strong practical experience with geometric deep learning and graph neural networks (GNNs); proven ability to tailor these methods to biological data, especially transcriptomics.
• Expertise in PyTorch with the ability to build and deploy
• Familiarity with developing production-quality pipelines, cloud computing, and model deployment best practices.
• Excellent communication skills and the ability to work cross-functionally with engineers, biologists and other key stakeholders to convert raw data output into neuroscience discoveries
• Experience (or strong interest) in drug discovery or biomedical research is a plus.
• Demonstrated ability to research and implement novel deep learning…