Translational Genomics AI ML Scientist

We are seeking an innovative and highly motivated Translational Genomics AI/ML Scientist based in Durham, NC, to develop next-generation computational capabilities that connect advanced genomics, machine learning, and biological interpretation to real-world crop research and breeding decisions. This role will transform large-scale genomic and phenotypic data into scientific insight, predictive models, and reusable digital tools that accelerate trait discovery, improve genomic prediction, and support innovation across Seeds R&D.

The successful candidate will work at the intersection of computational biology, AI/ML, bioinformatics, data science, and scientific product development. They will design methods for representing and learning from complex biological data, including genomic variation, gene annotations, expression data, pathways, phenotypes, and literature-derived evidence.

This position requires a strong scientific foundation and the ability to translate ambiguous research questions into scalable analytical approaches, validated models, and practical decision-support capabilities. The role will collaborate closely with geneticists, breeders, molecular biologists, data scientists, software engineers, and product teams to ensure that advanced computational methods are scientifically meaningful, technically robust, and adopted by research users.

The ideal candidate is a creative, hands-on scientist who combines deep expertise in genomics or computational biology with modern AI/ML skills and a passion for applying emerging technologies to high-impact biological problems. They can move fluidly between research innovation, method development, scalable implementation, stakeholder communication, and strategic scientific problem solving.

• Lead the scientific design and delivery of AI/ML-enabled genomics capabilities that translate large-scale genomic, phenotypic, and biological datasets into actionable insights for trait discovery, genomic prediction, and breeding decisions.
• Develop, validate, and operationalize advanced computational methods, including graph machine learning, foundation models, knowledge graphs, and explainable AI approaches, ensuring they are scientifically rigorous, reproducible, and fit for research and product adoption.
• Build scalable bioinformatics and machine-learning workflows that integrate genomic variation, annotations, expression data, pathways, phenotypes, and literature-derived evidence across cloud and enterprise data platforms.
• Advance computational genomics approaches by creating scalable representations, feature-engineering strategies, and predictive models that capture biological complexity and support research decision-making.
• Partner with geneticists, breeders, molecular biologists, data scientists, software engineers, and product teams to translate complex biological questions into practical analytical solutions and reusable digital capabilities.
• Communicate methods, assumptions, results, limitations, and biological interpretation clearly to technical and non-technical stakeholders, enabling confident scientific decision-making.
• Drive innovation by identifying emerging AI/ML, genomics, and scientific-computing approaches that can accelerate discovery, improve predictive performance, and strengthen Syngenta’s digital genomics platforms.
• Ensure solutions follow good scientific, data governance, engineering, and reproducibility practices, including clear documentation, version control, model validation, and transparent handover to collaborators or product teams.

• PhD in Bioinformatics, Computational Biology, Genomics, Genetics, Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related discipline.
• Strong background in genomics, quantitative genetics, population genetics, or computational biology.
• Demonstrated expertise in machine learning and statistical modeling with proficiency in Python and scientific computing.
• Experience working with large-scale biological datasets.
• Strong publication or research track record in computational genomics, AI/ML, or a related field.
• Excellent communication and collaborative problem-solving skills.

We’re especially interested in candidates who bring experience in one or more of the following areas:

• Genomics and graph-based genomic representations, including GNNs, GATs, graph embeddings, or network analytics.
• Foundation models, transformers, DNA language models, or biological large…