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# ML Researcher

- **Company:** Monarch
- **Location:** Emeryville, CA, United States
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, Software Tools, Reinforcement Learning
- **Published:** September 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d796429ad1d9f47e

## About the Role

* 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

## Description

* 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


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