Protein Design Scientist

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. 

Job Description

The Syngenta Seeds Bioinformatics group is seeking a Protein Design ML Scientist to join our growing protein design team in Durham, NC. In this role, you will drive agricultural trait discovery by developing and implementing state-of-the-art ML algorithms for computational protein engineering and other challenging biological problems. You will implement predictive models and active learning workflows, collaborating closely with experimentalists to design and optimize variant libraries even when experimental data is limited. Your expertise in protein language modeling and property predictive models AI will directly deliver next-generation traits to the field.  

Accountabilities: 

• Develop biological hypotheses and create scalable computational workflows for variant design and property prediction to accelerate trait discovery.
• Implement, adapt, and optimize advanced biomolecular machine learning models, including protein language models, generative models, co-evolutionary architectures, and three-dimensional structure prediction tools.
• Apply computational protein design and machine learning approaches to advance innovative projects within the trait development pipeline.
• Partner with wet-lab research teams to design high-quality variant libraries aligned with experimental objectives.
• Use active learning and Bayesian optimization techniques to integrate experimental screening results into iterative design and testing cycles.
• Analyze computational and experimental data to identify meaningful insights and guide future protein design strategies.
• Translate complex machine learning concepts, technical protocols, and analytical findings for both technical and non-technical audiences.
• Provide clear project updates, recommendations, and scientific insights to cross-functional stakeholders.
• Monitor developments in protein design research and evaluate promising tools, technologies, and project concepts for potential application.

Qualifications

• Ph.D. in Bioinformatics, Computational Biology, Biochemistry, Biophysics, or a related scientific discipline with at least 1 year of experience focused on protein sequence or structure modeling.
• Demonstrated hands-on experience developing, adapting, or fine-tuning machine learning models for protein sequence design, variant library generation, protein property prediction, or related biomolecular engineering applications.
• Deep understanding of protein sequence, structure, and function relationships, with the ability to apply this knowledge to computational protein design challenges.
• Proven ability to translate complex biological problems into actionable machine learning and deep learning hypotheses that support research and product development objectives.
• Strong collaboration and communication skills, with a track record of partnering effectively with cross-functional teams, including experimental and wet-lab scientists.
• Proficiency in Python and deep learning frameworks such as PyTorch, JAX, or similar tools, including experience adapting and fine-tuning open-source machine learning models.

Desired Qualifications: 

• Experience integrating protein structural information into computational workflows, including the use of structure prediction tools (e.g., AlphaFold, Boltz), structure-aware design methods (e.g., ProteinMPNN, Graph Neural Networks, structure-conditioned protein language models, Foldseek 3Di), or physics-based modeling approaches such as Rosetta and molecular dynamics simulations.
• Familiarity with wet-lab protein engineering workflows, including library design, screening data generation, model training datasets, and experimental validation of designed protein variants.
• Experience working with large-scale biological datasets, cloud computing environments (AWS), high-performance computing (HPC) systems, and containerized workflows such as Docker, Singularity, or Nextflow.
• Prior experience in agricultural biotechnology, plant biology, or a demonstrated interest in applying computational protein design technologies to crop science and agricultural innovation.
• Experience building scalable computational pipelines and workflows to support protein engineering research and data-driven discovery.
• Demonstrated ability to operate in a multidisciplinary research environment, partnering across computational, biological, and engineering teams to advance scientific objectives.

Additional Information

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: https://www.youtube.com/watch?v=OVCN_51GbNI 

Syngenta 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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