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
Skills Required
- Ph.D. or equivalent research record in machine learning, statistics, computational science, or a closely related field
- Ability to formulate open-ended research questions, build strong baselines, and design evaluations that withstand 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
- 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 judgment and comfort abandoning ideas when evidence does not support them
What We Do
Monarch Crops is building a spatial repellent that protects crops from insects, humans from toxins, and insects from needless harm.
