> Markdown version of [/jobs/ext/3596365-machine-learning-researcher-genomic-ai](https://www.wearedevelopers.com/jobs/ext/3596365-machine-learning-researcher-genomic-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Researcher, Genomic AI - **Company:** Bayer AG - **Location:** St. Louis, MO, United States - **Salary:** $110,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Bioinformatics, Cloud Computing, Computer Clusters, Computational Biology, Data Integration, Distributed Computing Environment, Machine Learning, Language Modeling, Population Genetics, Tensorflow, Graph Neural Networks, Pytorch, Transfer Learning, Large Language Models, Deep Learning, Gaussian, Containerization, Information Technology, Machine Learning Operations, Feature Extraction, Api Design - **Published:** October 6, 2026 - **Apply:** https://dejobs.org/x/x/D4A2831E03CA430D8308F0E69816B64B/job/ ## About the Role * 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 TensorFlow) and familiarity with large-scale model training (distributed training, GPU clusters). * Working knowledge of molecular biology fundamentals sufficient to interpret model outputs in biological context (e.g., gene regulation, variant consequence, population genetics). * Strong written/verbal communication and cross-disciplinary collaboration skills. Preferred: * Hands-on experience developing or fine-tuning genomic language models or biological foundation models (e.g., GPN, PlantCaduceus, Nucleotide Transformer, Evo, Enformer, AlphaGenome or similar large-scale sequence architectures for genomic prediction and functional track prediction). * Experience with functional genomics data: ATAC-seq, ChIP-seq, Hi-C, single-cell transcriptomics, or CRISPR screen data. * Background in quantitative genetics or genomic prediction (e.g., GBLUP, Bayesian alphabet models, marker-effect estimation) and understanding of breeding program workflows. * Familiarity with multi-omic data integration methods (e.g., multi-modal autoencoders, contrastive learning across modalities, graph neural networks on biological networks). * Knowledge of pangenomics, structural variant calling, or comparative genomics across crop species. * Experience with self-supervised, semi-supervised, or transfer learning strategies for data-efficient modeling in biology. * Familiarity with interpretability/explainability methods (attention visualization, in-silico mutagenesis, feature attribution) to derive biological hypotheses from model internals. * Exposure to classical ML approaches (gradient-boosted methods, kernel methods, Gaussian processes) as complementary or baseline tools. * Experience with model deployment in production (MLOps pipelines, containerization, API development, cloud/HPC infrastructure). * Track record of interdisciplinary collaboration with experimental biologists, resulting in validated biological predictions. ## Description 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., 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. * 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.