Machine Learning Researcher, Genomic AI

Bayer; United States; On-site; USD 110,000 - 150,000; Full time

Job summary

Bayer in the United States is seeking a Machine Learning Researcher to develop deep learning models for genomic and multi‑omic data, including large genomic language models. You will collaborate with biologists, breeders, and computational scientists to translate sequence data into actionable insights for genomic selection and editing strategies.

The role emphasizes model design, training, deployment, and clear communication of complex results, with a strong focus on biological interpretability

Qualifications

• PhD in a relevant field with demonstrated application to biological data.
• Experience building and training deep learning models on biological sequence or omic data.
• Proficiency with modern DL frameworks and large-scale model training.
• Ability to interpret model outputs in a biological context.

Responsibilities

• Design, train, and evaluate DL models on whole-genome sequences, expression data and multi-omics.
• Develop and fine-tune foundation models for DNA/RNA sequences to predict variant effects and traits.
• Build genotype–phenotype predictive models across environments and editing targets.
• Integrate heterogeneous data types into unified predictive frameworks.
• Collaborate with molecular biologists, geneticists, breeders and bioinformaticians.
• Work with engineering teams to deploy models in genomic pipelines and decision platforms.
• Communicate complex results through technical reports and presentations.

Skills

Genomic ML modeling

DL frameworks

Collaborative research

Education

PhD in Computational Biology

Tools

PyTorch

JAX

TensorFlow

Job description

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

• 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.

Who You Are

Required:

• 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.

Employees can expect to be paid a salary of approximately $110k-150k. Additional compensation may include a bonus or incentive program (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.. This salary (or salary range) is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.

This posting will be available for application until at least 10/16/26.

Bayer is an Equal Opportunity Employer/Disabled/Veterans

Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.

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Location: United States : Residence Based : Residence Based || United States : Missouri : Creve Coeur

Division: Crop Science

Reference Code: 872400

Email: [e-mail]