Domain-Aware ML Researcher for Genomics & Crops

Bayer CropScience Limited; Tulsa (OK); On-site; USD 120,000 - 170,000; Full time

Job summary

Bayer CropScience is seeking a Sr. Machine Learning Researcher to develop domain-aware models for agriculture, blending biology, genetics, and environmental data into robust AI systems.

You will design principled, interpretable architectures and uncertainty quantification methods, and collaborate with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate research into production within breeding and discovery pipelines.

Responsibilities

• Design domain-aware ML models that incorporate prior scientific knowledge into learning algorithms for agricultural and genomic applications.
• Develop novel architectures and loss functions embedding biological constraints into training.
• Architect models using high-dimensional genomic, phenomic, and environmental data to predict complex trait outcomes.
• Implement uncertainty quantification frameworks to provide calibrated confidence estimates on model predictions.
• Collaborate with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate domain expertise into model architecture decisions.
• Transition research prototypes into production-grade models integrated within breeding pipelines, ensuring reproducibility and maintainability.
• Contribute to publications and participate in the internal scientific community, staying at the frontier of scientific ML methodology.
• Prepare technical documentation and present findings to both technical and non-technical stakeholders.

Job description

Bayer CropScience is seeking a Sr. Machine Learning Researcher to develop domain-aware models for agriculture, blending biology, genetics, and environmental data into robust AI systems.

You will design principled, interpretable architectures and uncertainty quantification methods, and collaborate with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate research into production within breeding and discovery pipelines.