ML Researcher
Read straight from Monarch Crops's careers page, verified 1d ago.Seekless can tailor your résumé to it.
Seekless can write your résumé for this posting first.
Before you apply
SalaryNo pay range posted
LocationEmeryville, California
Work modeon-site
Typefull-time
SenioritySenior
DepartmentR&D
EquityIncluded
Company size10+ people
PostedSep 4, 2026 · 2mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
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
Python
About the company
We offer opportunities to do your life’s work while helping solve one of the most important technical and moral challenges of our time.
About the role
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?
Responsibilities
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
Requirements
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
Nice to have
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
To learn more, visit monarchlabs.org