Data Scientist, Bioengineering

About This Role

This role involves developing rigorous data-analysis pipelines that provide actionable insights for experimental and computational researchers. The Data Scientist will work at the intersection of Ultrasound, synthetic biology, and research platforms, collaborating with domain experts to transform raw data into valuable artifacts and develop ML pipelines for de-novo design and active learning cycles in bioengineering.

Responsibilities

• Collaborate with wet-lab scientists to define tractable optimization objectives and metrics, and encode domain specific priors and constraints for downstream computational modeling
• Stay current with research in Synthetic Biology and ML-guided molecular and cellular engineering, as well as data-analysis methods and techniques for biological data (OMICS, Agentic-workflows)
• Contribute to the long-term research roadmap and serve as a thought-leader for scientists

Requirements

• Deep grounding in synthetic biology, molecular engineering, and computational methods for data-analysis
• Experience in biomolecular ultrasound
• Familiarity with fundamental concepts in NGS, Omics, ML, and molecular engineering
• Proficiency in Python / PyTorch / BoTorch / Pyro and comfort writing clean, reproducible production grade code
• Experience bridging machine learning and experimental science, especially working with sparse, noisy, and or high-cost data

About the Company

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. They are developing fundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use. Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, they are building a cross-functional data-science group which sits at the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data engineers to create ML frameworks that accelerate molecule discovery and device optimization.