You’ll translate our hit Monarch repellent compounds from milligram-scale discoveries into pilot and manufacturing scale formulations. You’ll optimize solvent systems, work on controlled release systems, and ensure robust, reproducible processes for field trials and commercial production. 

Key Responsibilities

• Lead bench-to-pilot scale-up of candidate compounds, focusing on solvent selection, solvent recovery, controlled release, and other critical scale-up variables 

• Design and execute experiments to assess solubility, stability, and volatility 

• Troubleshoot formulation issues and iterate quickly under real-world constraints

• Collaborate cross-functionally with predictive-modeling, behavioral assay, and computational chemist groups 

Required Qualifications

• M.S. or Ph.D. in Organic Chemistry, Chemical Engineering, or related field

• 3+ years’ hands-on scale-up or process chemistry experience

• Deep understanding of solvent properties, controlled release phenomena, and formulation principles 

• Track record of developing robust, reproducible processes 

• Strong data analysis, documentation, and troubleshooting skills

Desired Attributes

• Entrepreneurial mindset: thrives in ambiguity and high-pressure environments

• Resilient problem-solver: tackles “hardest things” head-on 

• Communicates clearly, honestly, and respectfully 

• Collaborative spirit

About Monarch

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.

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