Principal Computer Vision Engineer

At Syngenta Crop Protection, we're pioneering solutions that safeguard global food security while championing sustainable agriculture. As a world market leader headquartered in Switzerland, we empower farmers with innovative crop protection technologies that defend against nature's toughest challenges. We unite advanced science with digital solutions to develop intelligent crop protection that maximizes yields while minimizing environmental impact. Join our mission of revolutionizing plant protection from seed to harvest. Syngenta has been ranked as a top employer by Science Magazine and it has been awarded with the "Friendly Work Space" label to all its Swiss sites. This role is primarily based in Basel, Switzerland, but offers remote flexibility and can be filled by candidates located in the US or the UK.

We're looking for a Principal Computer Vision Engineer to lead the design, development, and scaling of our Digital Biological Assessment systems — one of the most advanced drone-based computer vision platforms in agriculture R&D. You will process hundreds of terabytes of aerial imagery annually, build machine learning models that replace manual field assessments, and lead a team of engineers pushing the boundaries of what's possible in precision agriculture. What You'll Do Lead a team of computer vision engineers, setting technical direction and developing talent Architect and maintain end-to-end image analysis systems — from drone capture through ML inference to data output Write production-quality Python code for image processing, photogrammetry pipelines, and ML model deployment Design and train machine learning models for plant counting, height measurement, health assessment, and disease/weed detection from aerial imagery Manage AWS cloud infrastructure supporting large-scale image processing workloads Drive the integration of emerging technologies (large vision-language models, foundation models) into the assessment pipeline Collaborate cross-functionally with R&D biologists, IT, and operational teams to translate scientific needs into technical solutions Communicate the team's vision and results to stakeholders at all levels, including executive leadership

Ph.D. or M.Sc. in Computer Science, Machine Learning, Remote Sensing, or related field 10+ years of experience in computer vision and data engineering, with deep ML expertise 3+ years of people management experience leading technical teams Strong hands-on Python and Linux skills — you still write code and make architectural decisions Experience with photogrammetry (Metashape, Pix4D, or equivalent) and/or drone-based remote sensing Proven ability in building and operating large-scale image processing pipelines on AWS Excellent communication skills — you can explain complex technical concepts to non-technical stakeholders and influence across organizational boundaries Fluent English (written and spoken) Bonus Points Experience in agriculture, crop science, or precision farming applications Background in multispectral/hyperspectral sensor data analysis Experience with large vision-language models or foundation models for vision Experience in fast-moving startup environments

Why Syngenta? Unmatched scale: Few teams anywhere process computer vision at this volume — hundreds of terabytes of drone imagery per year across global field trials Real-world impact: Your work directly accelerates the development of crop protection products that help feed the world sustainably Innovation freedom: Strong leadership support, dedicated budget, and room to explore cutting-edge AI/ML approaches Flexible work: Remote-friendly with occasional travel to Basel (1–2x/year). We prioritize the best talent regardless of location Growth path: Clear trajectory to R&D or Digital leadership roles at the leadership team level Please send your complete application with English CV, diploma and work certificate copies (if applicable). 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.