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.

Accountabilities

• 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 Syngentas 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.

Qualifications :

Required Qualifications

• 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.

Desired Qualifications

Were 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 language models.
• Knowledge graphs semantic technologies biological ontologies or AI-assisted scientific research workflows.
• Cloud computing and production AI environments including AWS SnowFlake distributed computing or MLOps.
• Plant genomics breeding crop science or agricultural biotechnology.

Additional Information :

•  Authorized to work in the U.S. without sponsorship

What We Offer

• A culture that celebrates belonging and collaboration promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs.
• Full Benefit Package (Medical Dental & Vision) that starts your first day.
• 401k plan with company match Profit Sharing & Retirement Savings Contribution.
• Paid Vacation Paid Holidays Maternity and Paternity Leave Education Assistance Wellness Programs Corporate Discounts among other benefits.

Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: is an Equal Opportunity Employer and does not discriminate in recruitment hiring training promotion or any other employment practices for reasons of race color religion gender national origin age sexual orientation marital or veteran status disability or any other legally protected status.

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Remote Work :

No

Employment Type :

Full-time