Protein Design Scientist, Machine Learning

Company Description At Syngenta Seeds Field Crops, we're shaping the future of agriculture and empowering farmers to meet the ever-growing demand for food and fuel. We’re a global Ag Tech powerhouse, headquartered in the United States, with passionate, local experts collaborating with farmers to deliver solutions that create market opportunities. We unite precision breeding, advanced biotechnology trait choice, and digital platforms for unmatched in-field performance. Our seeds help mitigate risks such as disease, insect, weed, and extreme weather pressures, all while promoting sustainable farming practices that protect and enhance our planet. Join our mission of revolutionizing food security and transforming agriculture.

As a Protein Design Scientist, you leverage an AI-first approach, utilizing protein language models (pLMs) and generative sequence design to explore sequence-function relationships and pioneer next-generation agricultural traits. As an Applied ML Scientist, you are a hypothesis-driven scientist who leverages and adapts open-source machine learning models to biological data, addressing complex biological questions where data may be sparse and expensive to generate. You are also a collaborative team player who thrives in the dry-to-wet lab loop by turning agricultural and trait challenges into practical machine learning hypotheses and projects, while translating complex ML concepts and outputs into clear, practical suggestions for diverse stakeholders. Position will be located at Durham, North Carolina with an opportunity for remote work. Accountabilities • Design & Optimize: Formulate biological hypotheses and design computational workflows for large scale variant design and property prediction to accelerate trait discovery • Deploy ML Models: Implement, adapt, and tune state-of-the-art biomolecular ML models—including single-sequence LMs, generative models, co-evolutionary aware architectures, and 3D structural prediction models—to drive innovative projects for the trait pipeline • Collaborate Cross-Functionally: Partner closely with wet-lab research teams to design variant libraries, leveraging active learning and Bayesian optimization to iteratively integrate experimental screening data into design loops • Communicate Insights: Communicate complex deep learning concepts, protocols, and project progress clearly to technical and non-technical stakeholders • Innovate: Monitor the rapidly changing protein design literature and bring promising new tools and project ideas to the team