Machine Learning Engineer at SyngentaGroup

At Syngenta, we are building the most collaborative and trusted team in agriculture to provide leading seeds innovations that enhance the prosperity of farmers worldwide. Our Data Science and Engineering team in R&D Digital is seeking a motivated Machine Learning Engineer who will drive the development and deployment of advanced computer vision and machine learning solutions, with a primary focus on leveraging multi-modal imagery and sensor data to accelerate breeding programs and bring superior seeds to market faster. As an individual contributor, you will use your technical expertise and scientific rigor to transform raw imagery and other multiple data sources into scalable, production-grade AI tools that empower internal and external users across research, product development, and operational workflows. This work spans not only developing research prototypes but also building and maintaining the underlying software and cloud components (data pipelines, orchestration, deployment, monitoring) required to run reliably in production. To do so, you will engage directly with stakeholders, researchers, product managers, and technical partners to translate business objectives and scientific goals into robust, innovative machine learning solutions. You will also drive the strategic vision for next-generation phenomics and related AI capabilities, ensuring alignment with organizational goals and maximizing impact across multiple disciplines. This is an opportunity to apply cutting-edge remote sensing and AI technologies to solve real-world agricultural challenges on a global scale. Accountabilities:  • Design, develop, and deploy production-grade computer vision models that extract quantitative digital traits from multi-modal imagery (e.g., RGB, multispectral, thermal, hyperspectral, LiDAR, 3D point clouds) captured from drones, ground-based platforms, mobile devices, satellites and other kinds of sensors. • Build and maintain scalable phenomics pipelines that process thousands of field plots across multiple breeding programs, integrating image acquisition, preprocessing, trait extraction, quality control, and delivery to downstream data products with minimal manual intervention. • Collaborate with plant breeders, researchers, product managers, engineers, and data scientists to translate objectives into computer vision and machine learning solutions, validate outputs against ground truth, and ensure scientific and business relevance. • Shape the strategic direction for computer vision in phenomics, defining how to maximize value from proprietary imagery and sensor data through modern ML approaches (self-supervised learning, multi-modal fusion) while balancing innovation with practical deployment needs. • Contribute across the full lifecycle of machine learning projects, such as problem definition, data exploration, model selection, performance evaluation, deployment, and monitoring, which could include both phenomics and broader AI/ML applications. • Design, build, and own cloud-based data pipelines and workflow orchestrators to ingest, validate, transform, and deliver imagery and sensor-derived features at scale. • Drive productionalization of research code into maintainable services and pipelines, and optimize existing machine learning systems for performance, scalability, and reliability by applying best practices in software engineering, MLOps/CI-CD, containerization, infrastructure-as-code, and cloud deployment. • Architect and deploy mobile-first AI products that enable breeders to capture images and receive real-time identification, classification, or trait measurements. • Develop and operate automated image preprocessing and quality-control workflows to reliably transform raw imagery into analysis-ready data. • Contribute to knowledge sharing, documentation, and team learning, communicating complex machine learning concepts to non-technical stakeholders and supporting the team's knowledge base. • Follow an agile way of working and collaborating effectively across disciplines and global teams.