Research Engineer, Field Simulation

Monarch; Emeryville (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity

Job summary

Monarch in Emeryville, CA is seeking a researcher to build computer simulations that predict field performance of compounds. You will work with scientists to connect release, transport, and insect behavior, and test predictions against experimental data.

Develop reproducible simulation and analysis software, calibrate models with experimental data, and quantify uncertainty to support decision-making in real-world field trials.

Qualifications

• Proven experience building numerical simulations of physical or biological systems.
• Strong programming skills in Python, C++, or comparable tools.
• Solid foundation in modeling methods (fluid dynamics, transport, DEs, stochastic or agent-based).
• Experience estimating parameters, handling noisy data, and assessing predictive performance.
• Ability to work across disciplines to integrate chemical and biological understanding into simulations.

Responsibilities

• Develop models linking compound properties, formulation, release, and field responses.
• Simulate environmental conditions and their effects on chemical transport and insect behavior.
• Link predicted exposure to insect movement and behavior using experimental evidence.
• Collaborate with chemists, entomologists, and experimenters to calibrate models and plan experiments.
• Evaluate predictions against experiments and adjust uncertainty and decision thresholds.
• Build reproducible simulation and analysis software to compare compounds, formulations, and trial designs.

Skills

Numerical simulations

Python

C++

Uncertainty quantification

Cross-disciplinary

Education

Master’s or PhD in applied math/physics/engineering

Tools

Python

C++

Job description

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.

Build computer simulations that help us predict whether a compound that works in the lab will work in the field. The central question is practical: how will a compound affect an insect under real field conditions, and how much can we learn before running the trial? Work with our scientists to connect how compounds are released, move through the environment, and influence insect behavior, then test those predictions against experimental data.

Key Responsibilities

• Develop models that connect compound properties, formulation, release rates, and measured insect responses to field performance.
• Simulate the conditions that matter for each application, including wind, temperature, humidity, sunlight, rainfall, and vegetation, and their effects on chemical transport and persistence.
• Connect predicted exposure to insect movement, approach, landing, feeding, or egg laying using species-specific experimental evidence.
• Work with chemists, entomologists, and experimental scientists to calibrate models, identify missing measurements, and design experiments that resolve the largest uncertainties.
• Evaluate predictions against held-out experiments and prospective field trials. Quantify uncertainty and establish when a model can support a decision and when more evidence is needed.
• Build reproducible simulation and analysis software that helps the team compare compounds, formulations, application methods, and field-trial designs.

Qualifications

• Master’s, Ph.D., or equivalent research and engineering experience in applied mathematics, physics, mechanical or chemical engineering, computational science, or a closely related field.
• Demonstrated experience building numerical simulations of physical or biological systems and checking their predictions against experimental observations.
• Strong scientific programming skills in Python, C++, or comparable tools, with the ability to build reliable, reusable software.
• A strong foundation in relevant modeling methods, such as fluid dynamics, transport processes, differential equations, stochastic modeling, or agent-based simulation.
• Experience estimating model parameters, working with noisy experimental data, and assessing uncertainty and predictive performance.
• Ability to work across disciplines and develop the chemical and biological understanding needed to make the simulations useful.

Desired Attributes

• Experience with chemical release and transport, atmospheric or environmental modeling, agricultural systems, insect behavior, or chemical ecology.
• A record of translating simulations into predictions that hold up in physical experiments, including in settings where conditions change.
• Experience combining mechanistic models with machine learning or surrogate models to make simulation and experiment selection more effective.
• Good judgment about which processes need detailed modeling and where a simpler model is sufficient.
• Intellectual rigor and comfort changing an approach when the evidence does not support it.

To learn more, visit monarchlabs.org