AI Engineering Team Lead

Company Description

Syngenta Seeds is one of the world’s largest developers and producers of seed for farmers, commercial growers, retailers and small seed companies. Syngenta seeds improve the quality and yield of crops. High-quality seeds ensure better and more productive crops, which is why farmers invest in them. Advanced seeds help mitigate risks such as disease and drought and allow farmers to grow food using less land, less water and fewer inputs.

Syngenta Seeds brings farmers more vigorous, stronger, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions.

Role purpose

Lead the development, deployment and operationalization of artificial intelligence, machine learning, and advanced computational methods within the Bioinformatics group to support Seeds R&D in China. The role is responsible for building and growing a team to provide scalable, production-ready AI solutions across biological data domains including genomics, multi-omics, and imaging. By working closely with bioinformaticians, breeders, molecular biologists, and digital partners, the AI Engineering Team Lead translates biological challenges in the Seeds pipeline into robust computational solutions that accelerate discovery, improve decision making, and enhance research productivity.

ACCOUNTABILITIES

• Team Leadership & Capability Development. Recruit, mentor, and lead a high-performing team of AI scientists and engineers. Establish technical standards, engineering best practices, and reusable AI frameworks, components, and capabilities. Foster collaboration between China and global bioinformatics teams, and develop capabilities in machine learning, deep learning, cloud computing, and scientific software engineering to support long-term Seeds R&D objectives.

• AI Model Development & Innovation. Lead the design, development, and application of machine learning and deep learning approaches for biological data, including genomic and protein sequence modeling, computer vision, multi-omics integration, scientific knowledge mining and retrieval, and multimodal AI. Drive the evaluation, adaptation, and implementation of emerging AI methods to create measurable value for breeding and biotechnology programs.

• AI Platforms, Engineering & Operations. Establish and maintain scalable AI platforms, workflows, and engineering practices that cover model lifecycles from data preparation, model training, distributed computing, evaluation, reproducibility, deployment, monitoring, and maintenance. Partner with Seeds R&D Engineering Enablement & Operations to leverage cloud and HPC infrastructure, ensuring AI workloads are delivered through reliable, scalable, and cost-effective computing environments that support Seeds R&D research and product development.

• Trait Pipeline & Breeding Enablement. Partner with breeders, trait discovery scientists, molecular biologists, and bioinformaticians to identify high-value opportunities where AI can accelerate decision-making and scientific discovery. Translate biological challenges related to genomic prediction, genotype-to-phenotype relationships, biotechnology trait discovery, protein engineering, and experimental design into practical AI-enabled solutions.

• Cross-Functional Collaboration & Technology Integration. Collaborate with Bioinformatics, Traits Digitalization & Genomics, Data Science, and research functions to integrate AI capabilities into existing scientific workflows and platforms. Ensure AI solutions are effectively adopted meeting internal standards, interoperable with enterprise systems, and deliver measurable value to breeding and biotechnology programs.

• AI Strategy & Technology Scouting. Monitor developments in artificial intelligence, scientific foundation models, and computational biology. Evaluate emerging technologies and identify opportunities to accelerate breeding and biotechnology research through adoption of new AI methods, platforms, and external partnerships.

Knowledge, Experiences & Capabilities

• Knowledge:
  • Strong understanding of machine learning, deep learning, generative AI, and modern AI approaches for prediction, design, and knowledge discovery.
  • Knowledge of AI applications across biological data domains, including genomic and protein sequences, multi-omics datasets, biological imaging, and scientific literature.
  • Understanding of bioinformatics and computational biology, including genomics, transcriptomics, genomic selection, protein biology, and biological data management.
  • Understanding of modern AI platforms, including distributed computing, MLOps, cloud/HPC environments, and scalable data engineering practices.
  • Awareness of emerging advances in artificial intelligence, scientific foundation models, and computational biology.
• Education and Experience
  • Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Bioinformatics, Computational Biology, or a related quantitative discipline.
  • 5+ years of experience in AI, machine learning, bioinformatics, computational biology, or scientific software development, including experience leading technical projects and/or teams.
  • Demonstrated success developing and deploying AI solutions in research or production environments.
  • Experience applying AI to biological problems involving genomics, protein science, imaging, multi-omics data, or scientific knowledge discovery.
  • Experience working in multidisciplinary environments spanning biology, engineering, data science, and research functions.
  • Experience in agriculture, biotechnology, life sciences, or related research-intensive industries is preferred.
  • A strong track record of scientific innovation, demonstrated through impactful publications, patents, technology development, or successful application of AI to biological research, is highly desirable.
• Capabilities
  • Proficiency in Python and modern software engineering practices, with experience developing and deploying machine learning solutions using frameworks such as PyTorch, TensorFlow, or JAX.
  • Experience leveraging cloud and HPC environments to support large-scale AI workloads and scientific computing.
  • Familiarity with AI platform technologies, workflow automation, model lifecycle management, reproducibility, and deployment practices.
  • Ability to build and develop high-performing technical teams while establishing engineering standards and best practices.
  • Strong communication skills with the ability to translate complex computational concepts into practical scientific solutions for diverse stakeholders and senior leadership.
  • Excellent written and spoken English.

Note: 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, gender identity, marital or veteran status, disability, or any other legally protected status.

To learn more visit: www.syngenta.com