About the job
Join Merge Labs, a pioneering research facility dedicated to merging biological and artificial intelligence to enhance human capabilities, agency, and experience. We aim to achieve this by crafting innovative brain-computer interfaces that communicate with the brain at high bandwidth, seamlessly integrate with cutting-edge AI, and prioritize safety and accessibility for all users.
About the Team:
At Merge Labs, we are on a mission to revolutionize brain-computer interfaces by leveraging advancements in synthetic biology, neuroscience, AI, and non-invasive imaging technologies. Our cross-functional data science team is situated at the convergence of computational modeling, neuroscience, and biomolecular engineering. This collaborative unit works closely with wet-lab scientists, automation specialists, and data engineers to develop machine learning frameworks that facilitate rapid molecule discovery and device enhancement.
About the Role:
We are seeking a talented Senior / Principal ML Scientist to architect and scale Bayesian optimization and reinforcement learning frameworks that guide molecular engineering initiatives through iterative design-build-test-learn (DBTL) cycles. You will start with a fresh approach to construct the company's closed-loop optimization infrastructure, establishing the data and modeling foundations that link experiments with these ML frameworks. Over time, you will transition prototypes into operational pipelines, significantly enhancing experimental throughput and discovery success across various biomolecular and neuroengineering sectors.
Key Responsibilities:
Develop the scientific and engineering framework for active learning and closed-loop optimization, encompassing data ingestion, ML modeling, and library design.
Collaborate with wet-lab scientists to establish feasible optimization objectives while incorporating domain-specific priors and constraints.
Create prototypes for representation learning and acquisition strategies utilizing both internal and public datasets; benchmark and validate the performance of models.
Integrate machine learning models with experimental data streams, making them accessible to non-domain experts for broader utilization.
Extend machine learning frameworks to accommodate multi-objective or constrained optimization challenges.
Stay abreast of the latest advancements in Bayesian optimization, active learning, and reinforcement learning, and prototype innovative algorithms to enhance the company's capabilities.
