Employer:

mZero.

Employment:

Full-time. In-office in Emeryville, California. Reports directly to the CEO.

Annual base salary:

$160,000

• 260,000, 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 Required 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