Lead Entomologist - Behavioral Assay Development
Job Overview
Monarch is seeking a Lead Entomologist to develop and implement behavioral assays that assess how insects respond to visual and olfactory stimuli. Your work will be critical in validating the efficacy of machine-learning-predicted compounds across multiple insect species.
Key Responsibilities
•Design and develop behavioral assays to assess insect response to predicted repellent compounds.
•Lead experiments to test compounds for broad-spectrum effectiveness
•Analyze experimental data to optimize compound performance, ensuring both repellency and pleasant odor characteristics.
•Present findings to the leadership team and contribute to the ongoing development of novel insect repellents.
Location
This is a full-time, on-site position based in Alameda, CA.
Why Join Monarch?
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.
Qualifications
•PhD or Master’s degree in Entomology or a related field
•Experience in behavioral assays and insect behavior research
•Experience working with machine learning models or cheminformatics pipelines is a plus
•Excellent analytical skills
•Strong work ethic
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.