Full-Time Machine Learning Engineer

At Syngenta, we believe every employee has a role to play in safely feeding the world and taking care of our planet. To support that challenge, we are currently seeking a Machine Learning Engineer.

As a Machine Learning Engineer at Syngenta, you will work within a multidisciplinary global team to discover, define, and design experiences that empower researchers to work more effectively and efficiently by utilizing our data-driven solutions.

Duties:

• Design, build, and maintain scalable machine learning solutions in production environments
• Contribute across the full lifecycle of machine learning projects, including problem definition, data exploration, model selection, performance evaluation, and deployment
• Develop robust, maintainable code for AI model serving and inference optimization
• Architect prompt engineering systems and LLM orchestration frameworks
• Collaborate with product managers and engineers to integrate ML systems into user-facing products
• Implement robust software engineering practices for ML systems including version control, testing, and CI/CD pipelines
• Stay current with the latest ML techniques and propose adaptations and improvements to internal practices

Requirements:

• Master's or Doctoral degree in Computer Science, Mathematics, Statistics, Engineering or a related field
• 5-8 years of experience in a machine learning engineering role with solid software development practices
• Proficiency with machine learning frameworks and libraries (e.g., PyTorch, TensorFlow, Keras, scikit-learn, XGBoost) and data manipulation (e.g. Pandas/Polars, SQL)
• Experience applying ML to both structured (tabular) and unstructured (text, image) data
• Knowledge of modern AI approaches including large language models, agentic orchestration, and generative AI applications
• Solid understanding of data fundamentals, machine learning algorithms, and statistical methods
• Strong programming skills in Python with emphasis on writing production-quality code, proficiency in SQL
• Knowledge of cloud services (e.g. AWS, GCP, Azure) and containerization technologies (e.g. Docker)

Preferred:

• Experience optimizing models for production deployment and inference
• Experience with ML monitoring, observability, and evaluation frameworks
• Experience implementing agentic AI systems
• Familiarity with LangGraph or similar agentic LLM orchestration frameworks
• Familiarity with vector databases and embedding models for information retrieval
• Experience fine-tuning language models

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