ML Research Scientist – De Novo Design
Listed on 2026-02-06
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Engineering
AI Engineer, Data Science Manager -
IT/Tech
AI Engineer, Machine Learning/ ML Engineer, Data Scientist, Data Science Manager
Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by 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.
About the team: 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, we 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.
About the role: We’re hiring a Senior / Principal ML Scientist to design and scale de novo design frameworks that guide molecular engineering campaigns through iterative design–build–test–learn (DBTL) cycles. Starting from a blank slate, you’ll first architect the company’s closed-loop optimization backbone—building the data and modeling foundations that connect experiments to these ML frameworks. Over time, you’ll help translate these prototypes into production pipelines that measurably improve experimental throughput and discovery success across multiple biomolecular and neuroengineering verticals.
In this role, you will:
- Build the scientific and engineering scaffolding in collaboration with data-engineering & MLOps for de novo design and closed-loop optimization, including data ingestion, ML modeling, and library design.
- Collaborate with wet-lab scientists to define tractable optimization objectives and encode domain specific priors and constraints.
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