We use AI to discover new pesticides, because the ones we have now are failing — pests keep evolving, and the chemicals kill too many other things (like humans). Our models replace slow, expensive lab work, letting us discover new molecules 100× faster. Our first pesticide targets a Spodoptera with a novel mode of action. We’re a small team of young engineers from Caltech & Wolfram Research, with experience in drug discovery ML models, rebuilding agrochemical R&D around AI.

We’re building a system that replaces a lab technician + physics with a single neural network. This role is for exceptional students who want to own high impact ML research and work directly with founders. You’ll be part of the core team. No side projects. No throwaway intern work. You’ll be expected to think deeply, write clean code, and help us discover safer pesticides for the world.

What You’ll Work On

• Design and train new ML model architectures for molecular discovery.
• Build pesticide discovery agents.
• Create autonomous data collection systems for high-quality training data.
• Design experiments to identify weaknesses in our models.

Qualifications

• Solid practical ML engineering and software engineering fundamentals (Python, PyTorch/JAX/TensorFlow, NumPy/SciPy)
• Strong foundation in mathematics, algorithms, statistics, and data science principles.
• Good prompter and up to date with LLM research.

Bonus:

• Prior research in developing novel foundation model architectures or training methods.
• Familiar with distributed training on cloud providers.
• Strong performance in math/programming competitions or coursework.

We use Python + PyTorch to build AI models that predict how well molecules will do at killing a pest and nothing else. We use these models to search through billions of possible compounds. Model accuracy and data quality are problems we're always iterating on. Another hard part is choosing which molecules to test in physical reality for data feedback — we're using ideas from information theory and reinforcement learning to pick the highest utility tests, trading off cost and time. On the bio side, we work on designing effective assays that are conducive to large scale data collection for ML, as well as optimizing the process of in-vivo testing on our target pest. Minimizing time between test results is a big priority. Long term, we’re building a general system that can design any molecule for a given task using AI.