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 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 Qualifications PhD with +1-year experience in bioinformatics, computational biology, biochemistry & biophysics, or a related field with a focus on protein sequence or structure modeling ML and p

LM Experience:

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 tasks

Protein Science Expertise:

Deep understanding of protein sequence-structure-function relationships

Scientific Problem Solving:

Proven ability to transform complex biological challenges into actionable, ML/DL computational hypotheses to support the trait pipeline

Collaborative Communication:

Strong communication skills with a track record of working effectively alongside experimental/wet-lab scientists

ML & Deep Learning Engineering:

Experience working with Python and deep learning libraries (like PyTorch or JAX) to adapt or fine-tune open-source models

Desired Qualifications:

Structural Integration and biophysics: Experience integrating protein structural information, including structure prediction models (AlphaFold, Boltz), structure-aware design methods (ProteinMPNN, GNNs, structure-conditioned pLMs, Foldseek 3Di), or physics-based approaches (Rosetta, molecular dynamics) to augment sequence-based design and prediction workflows.

Wet-Lab History:

Strong familiarity of or experience in wet-lab workflows-either in library design or generating screening data for model training or validating designed variants (e.g., protein characterization)-to facilitate seamless communication with experimental partners Compute and data: Familiarity querying large-scale biological datasets, working in cloud environments (AWS, HPC), and utilizing containerized workflows (Docker, Singularity, Nextflow)

Industry Experience:

Prior experience in agricultural biotechnology, plant biology, or a strong interest in translating computational protein design to crop science

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. WL 4B #

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