ML Researcher for Active-Learning in Experimental Design

Monarch; Emeryville (CA); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Equity

Job summary

Monarch in Emeryville, CA, is seeking a PhD-level researcher to advance machine learning for experimental design in biological settings. The role focuses on developing models that fuse molecular data, assay metadata, and behavioral signals to guide informative experiments.

You will balance theory and implementation, iterating on baselines and evaluations while translating findings into actionable scientific insights for laboratory teams.

Qualifications

• Ph.D. or equivalent research record in machine learning, statistics, or a closely related field.
• Demonstrated ability to formulate open-ended research questions, build strong baselines, and design evaluations that survive distribution shift.
• Strong Python skills and experience with a modern ML framework.
• Experience working with noisy, limited, multimodal, or experimentally generated datasets.
• Ability to move between theory, implementation, and scientific interpretation.

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 balancing efficacy, uncertainty, novelty, and information value.
• Define retrospective and prospective evaluations, includingholdouts by chemical scaffold, laboratory, colony, and time.
• Investigate which behavioral signals generalize across experiments and which reflect confounding or noise.
• Translate model failures into new labels, assay variants, controls, or experiments to improve next training cycle.
• Communicate results with enough precision that experimental scientists can understand why a recommendation should or should not be trusted.

Skills

Python

Machine Learning

Data Analysis

Research

Education

Ph.D. or equivalent

Tools

PyTorch

Job description

Monarch in Emeryville, CA, is seeking a PhD-level researcher to advance machine learning for experimental design in biological settings. The role focuses on developing models that fuse molecular data, assay metadata, and behavioral signals to guide informative experiments.

You will balance theory and implementation, iterating on baselines and evaluations while translating findings into actionable scientific insights for laboratory teams.