ML Researcher

Responsibilities

Research models that combine molecular information, formulation and dose, assay metadata, video-derived behavior, and laboratory context
Develop active-learning and sequential experiment-selection methods that balance predicted efficacy, uncertainty, novelty, and information value
Define retrospective and prospective evaluations, including holdouts by chemical scaffold, laboratory, colony, and time
Investigate which behavioral signals generalize across experiments and which reflect confounding, measurement noise, or laboratory-specific effects
Translate model failures into new labels, assay variants, controls, or experiments that improve the next training cycle
Communicate results with enough precision that experimental scientists can understand why a recommendation should or should not be trusted

Ph.D. or equivalent research record in machine learning, statistics, computational science, or a closely related field
Demonstrated ability to formulate open-ended research questions, build strong baselines, and design evaluations that survive distribution shift
Strong software skills in Python and a modern machine-learning framework
Experience working with noisy, limited, multimodal, or experimentally generated datasets
Ability to move between theory, implementation, and scientific interpretation

Experience with active learning, Bayesian optimization, reinforcement learning, causal inference, or scientific foundation models
Experience in molecular discovery, biology, animal behavior, robotics, or another domain where models learn from physical experiments
Track record of prospective validation rather than benchmark-only research
Strong research taste and comfort abandoning an attractive idea when the evidence does not support it
To learn more, visit monarchlabs.org