Machine Learning Researcher

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Machine Learning Researcher, Genomic AI 

We are seeking a Machine Learning Researcher with expertise in machine learning for biological systems, particularly genomic and multi-omic data modeling. This role is centered on building and deploying state-of-the-art AI models - including large-scale genomic language models and deep representation learning architectures - that extract actionable biological insight from complex molecular datasets. You will develop models that learn the grammar of genomes, predict functional consequences of genetic variation, and connect molecular signatures to whole-organism phenotypes across diverse crop species. This work directly supports genomic selection and genome editing target identification, turning sequence-level intelligence into breeding and discovery decisions at a global scale. 

YOUR TASKS AND RESPONSIBILITIES  

The primary responsibilities of this role are: 

• Genomic & Omic Model Development: Design, train, and evaluate deep learning models (LLMs, transformers, and representation learning architectures) on whole-genome sequences, gene expression profiles, epigenomic marks, k-mer spectra, skim-seq, pangenome graphs, and multi-omic integrations. 

• Genomic Language Models: Develop and fine-tune foundation models for DNA/RNA sequences that capture long-range dependencies and regulatory grammar to predict variant effects, gene function, and trait associations in crop genomes. 

• Genomic Selection & Editing Enablement: Build predictive models connecting genotype to phenotype across environments, identify high-value editing targets, and rank candidate genetic interventions with biological interpretability and statistical rigor. 

• Functional Data Integration: Integrate heterogeneous biological data types with high-resolution genome assemblies, structural variants, gene regulatory networks, protein structure predictions, and phenomic measurements - into unified predictive frameworks. 

• Interdisciplinary Collaboration: Work closely with molecular biologists, geneticists, breeders, bioinformaticians, and computational scientists to ground models in biological reality, design informative training data strategies, and validate predictions experimentally.