Research Scientist - Computational Chemistry

Research Scientist - Computational Chemistry

This range is provided by Monarch Crops. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

Monarch is seeking a Computational Chemist to lead our molecular modeling efforts to predict how insects respond to olfactory stimuli. Reporting directly to the Chief Technology Officer, your work will be critical in validating the efficacy of machine-learning-predicted compounds across multiple insect species.

• Develop and apply computational models to predict the molecular interactions between compounds and olfactory receptors
• Conduct quantum mechanical calculations, molecular docking, and dynamics simulations to refine predictions
• Analyze data from computational experiments to prioritize compounds for lab and field validation

This is a full-time, on-site position based in Oakland, CA.

Building an alternative to insecticides is one of the most important technical challenges of our time. Monarch is developing a product that works—a spatial repellent that protects crops from insects, humans from toxins, and insects from needless harm. If that mission motivates you, consider applying.

• PhD (or Master's degree + 7 years industrial experience) in biophysics, biochemistry, chemistry, engineering, or equivalent, with a computational emphasis
• Skilled in scientific programming (e.g., Python), data analytics, and reporting (experience with Pandas, Num Py, Jupyter)
• Proficiency in advanced molecular modeling techniques and associated software (docking, molecular dynamics simulations, rational design or ligand design). Experience with psi4, MDAnalysis, FreeSASA, Auto Dock Vina, Auto Dock 4.
• Familiarity with cheminformatics tools (Open Babel, RDKit) and basic machine learning/AI applications in computational chemistry, including protein structure prediction (Alpha Fold or similar)
• Strong work ethic
• Ability to work in a creative, fast-paced environment: prototyping ideas, iteratively optimizing them, and multi-tasking.

Building an alternative to insecticides is one of the most important technical challenges of our time. Most fruits and vegetables are sprayed with insecticides, including organics. The top three sprayed on these crops are toxic to the human nervous system. An estimated 35 quadrillion animals are killed yearly because of their use. And farmers lose tens of billions of dollars a year because insecticides often fail at their basic job: preventing insects from destroying crops.

Monarch is developing a product that works—a spatial repellent that protects crops from insects, humans from toxins, and insects from needless harm. It will work by preventing insects from landing on crops in the first place. We’re building a genomic, molecular, and behavioral dataset from the ground up. Then applying computational chemistry and machine learning tools to predict which of the billions of potential compounds in nature trigger a ‘fly away’ signal from the olfactory neurons in the antennae to the smell center in the animal’s brain.

From that unexplored data space, we’ll create the most effective products to protect crops and our long-term health.