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

Communicate results with enough precision that experimental scientists can understand why a recommendation should or should not be trusted

Qualifications

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

Desired Attributes

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