Protein Design Scientist: ML for Crop Traits (Remote)

Syngenta; Durham (NC); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Full Benefits Package

401k with company match

Paid Vacation & Holidays

Education Assistance

Wellness Programs

Corporate Discounts

Job summary

Syngenta Seeds Field Crops in Durham, NC is seeking a Protein Design Scientist and Applied ML Scientist to advance next-generation agricultural traits using AI-first approaches. You will work across the dry-to-wet lab loop and translate complex ML concepts into practical guidance for stakeholders.

The role emphasizes collaboration with wet-lab teams, development of variant libraries, and leveraging open-source ML models to biological data in a fast-paced environment, with remote-work flexibility.

Qualifications

• PhD in bioinformatics, computational biology, biochemistry, or biophysics with focus on protein sequence/structure modeling.
• ML/DL: hands-on experience delivering protein sequence design or related tasks.
• Deep understanding of protein sequence-structure-function relationships.
• Able to translate complex biological challenges into actionable ML hypotheses.

Responsibilities

• Formulate biological hypotheses and design computational workflows for large-scale variant design and property prediction.
• Deploy state-of-the-art biomolecular ML models to drive trait pipeline projects.
• Collaborate with wet-lab teams to design libraries and integrate experimental data into design loops.
• Communicate deep learning concepts and project progress to diverse stakeholders.
• Monitor protein design literature and bring new tools to the team.

Skills

PhD required

ML for protein design

Protein seq-structure

Problem solving

Collaboration

Python & DL

Education

PhD in bioinformatics/biochem/biophysics

Tools

PyTorch

JAX

AlphaFold

Rosetta

Job description

Syngenta Seeds Field Crops in Durham, NC is seeking a Protein Design Scientist and Applied ML Scientist to advance next-generation agricultural traits using AI-first approaches. You will work across the dry-to-wet lab loop and translate complex ML concepts into practical guidance for stakeholders.

The role emphasizes collaboration with wet-lab teams, development of variant libraries, and leveraging open-source ML models to biological data in a fast-paced environment, with remote-work flexibility.