Machine Learning Researcher, Genomic AI

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.

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.

• 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.
• Scalable Deployment:
  Partner with engineering and IT teams to operationalize models within genomic selection pipelines, editing nomination workflows, and decision-support platforms used by breeding programs globally.
• Documentation & Communication:
  Communicate complex modeling results to diverse audiences, prepare technical reports, and build organizational confidence in AI-driven biological discovery.

• PhD in Computational Biology / Bioinformatics, Genomics / Statistical Genetics, Machine Learning / Deep Learning, Computer Science (with focus on biological or sequential data), Biostatistics / Quantitative Genetics, Systems Biology, or a closely related quantitative discipline with demonstrated application to biological data.
• Demonstrated research experience building and training deep learning models on biological sequence data or high-dimensional omic datasets.
• Proficiency in modern deep learning frameworks (PyTorch, JAX, or Tensor Flow) and…