ML Researcher

We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.

Full-time, in-office in Emeryville, California. Compensation includes equity.

Develop the learning methods that turn repeated assays into better scientific decisions. The central question is prospective: can a model use prior compound, assay, and behavior data to recommend an experiment that is more informative than the one scientists would otherwise run?

Key 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