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. $160,000–$260,000 annual base salary plus 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