ML Research Scientist - Bayesian Optimization

Merge Labs • San Francisco, CA

11h

About The Position

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 Bayesian optimization and reinforcement-learning 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.

Requirements

• Strong grounding in probabilistic modeling, uncertainty quantification, and representation learning.
• Working knowledge of preference optimization and transfer-learning strategies
• Proficiency in Python / PyTorch / BoTorch / Pyro (or similar) and comfort writing clean, reproducible production grade code.
• Experience bridging machine learning and experimental science – working with sparse, noisy, and or high-cost data.
• A collaborative, systems-level mindset.

Nice To Haves

• Familiarity with neuroscience.
• Familiarity with language / state-space models.

Responsibilities

• Build the scientific and engineering scaffolding for active-learning 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.
• Prototype representation-learning and acquisition-strategy using internal and public datasets; benchmark and validate model performance.
• Integrate ML models with experimental data streams and serve to non-domain experts for model democratization.
• Extend ML frameworks to handle multi-objective or constrained optimization problems.
• Stay up-to-date with the latest research in Bayesian optimization, active learning, and RL, and prototype novel algorithms that can be deployed to improve the company’s discovery or development workflows.
• Contribute to the long-term research roadmap and serve as a thought-leader for scientists.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed