Research Scientist - Computational Chemistry

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.

Our mosquito work

Build the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed mosquito behavior. Your work should help us choose more informative compounds to test, not merely explain results after the fact.

Key Responsibilities

• Develop molecular representations and predictive models for compound effects on mosquito landing and related behavioral endpoints

• Combine chemical structures and descriptors with formulation, dose, assay, environmental, and behavioral data

• Design prospective evaluations that measure whether model-ranked compounds outperform conventional selection approaches

• Quantify uncertainty, identify out-of-domain predictions, and propose experiments that distinguish competing chemical hypotheses

• Partner with formulation chemists and machine-learning researchers to recommend the next compound, dose, formulation, or controlled variant to test

• Build reproducible computational workflows with traceable structures, descriptors, model versions, and experimental outcomes

Qualifications

• Ph.D. or equivalent research experience in computational chemistry, chemoinformatics, physical chemistry, medicinal chemistry, chemical engineering, or a related field

• Experience with molecular descriptors, similarity methods, QSAR, molecular machine learning, or graph-based models

• Strong Python skills and experience with tools such as RDKit or equivalent chemical-computing libraries

• Ability to design leakage-resistant evaluations and interpret model performance in chemical rather than purely statistical terms

• Clear scientific writing and close collaboration with experimental teams

Desired Attributes

• Experience with active learning, Bayesian optimization, uncertainty calibration, or prospective molecular discovery

• Knowledge of volatility, solubility, controlled release, odorants, or insect-active small molecules

• Experience connecting computation to iterative wet-lab experiments

• Interest in building open, reusable scientific methods rather than a one-time screening model

Our crop-protection work

Build the computational chemistry layer that connects molecular structure, physicochemical properties, formulation context, and observed insect behavior. Your work should help us choose more informative compounds to test, not merely explain results after the fact.

Key Responsibilities

• Develop molecular representations and predictive models for compound effects on insect landing on crops and related behavioral endpoints

• Combine chemical structures and descriptors with formulation, dose, assay, environmental, and behavioral data

• Design prospective evaluations that measure whether model-ranked compounds outperform conventional selection approaches

• Quantify uncertainty, identify out-of-domain predictions, and propose experiments that distinguish competing chemical hypotheses

• Partner with formulation chemists and machine-learning researchers to recommend the next compound, dose, formulation, or controlled variant to test

• Build reproducible computational workflows with traceable structures, descriptors, model versions, and experimental outcomes

Qualifications

• Ph.D. or equivalent research experience in computational chemistry, chemoinformatics, physical chemistry, medicinal chemistry, chemical engineering, or a related field

• Experience with molecular descriptors, similarity methods, QSAR, molecular machine learning, or graph-based models

• Strong Python skills and experience with tools such as RDKit or equivalent chemical-computing libraries

• Ability to design leakage-resistant evaluations and interpret model performance in chemical rather than purely statistical terms

• Clear scientific writing and close collaboration with experimental teams

Desired Attributes

• Experience with active learning, Bayesian optimization, uncertainty calibration, or prospective molecular discovery

• Knowledge of volatility, solubility, controlled release, odorants, or insect-active small molecules

• Experience connecting computation to iterative wet-lab experiments

• Interest in building open, reusable scientific methods rather than a one-time screening model

To learn more, visitmonarchlabs.org