ML Researcher
Monarch, Emeryville, California
What you will do
- 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