Genomic AI Researcher — Deep Learning for Genomes

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

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