[Hiring] Machine Learning Researcher @Bayer

Role Description

We are seeking a Machine Learning Researcher with expertise in machine learning for biological systems, with a particular focus on 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. Your work will directly enable transformative applications in genomic selection and genome editing target identification.

This position is being hired at the entry level. Depending on the candidate's depth of experience and demonstrated research impact, the role may be filled at the Senior Machine Learning Researcher level.

YOUR TASKS AND RESPONSIBILITIES

• Genomic & Omic Model Development:
  • Design, train, and evaluate deep learning models on diverse omic datasets.
• Genomic Language Models:
  • Develop and fine-tune foundation models for DNA/RNA sequences.
• Genomic Selection & Editing Enablement:
  • Build predictive models that connect genotype to phenotype across environments.
• Functional Data Integration:
  • Integrate heterogeneous biological data types into unified predictive frameworks.
• Interdisciplinary Collaboration:
  • Work closely with molecular biologists, geneticists, breeders, bioinformaticians, and computational scientists.
• Scalable Deployment:
  • Partner with engineering and IT teams to operationalize models within genomic selection pipelines.
• Research Contribution:
  • Advance the state of the art through publications and engagement with the broader computational biology and AI research community.
• Documentation & Communication:
  • Communicate complex modeling results to diverse audiences and prepare technical reports.

Qualifications

• PhD in one of the following or closely related fields:
  • Computational Biology / Bioinformatics
  • Machine Learning / Deep Learning
  • Genomics / Statistical Genetics
  • Computer Science (with focus on biological or sequential data)
  • Biostatistics / Quantitative Genetics
  • Systems Biology
  • Another 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).
• Working knowledge of molecular biology fundamentals sufficient to interpret model outputs in biological context.
• Strong communication skills and ability to collaborate effectively across disciplines.

Requirements

• Hands-on experience developing or fine-tuning genomic language models or biological foundation models.
• Experience with transformer architectures, long-context sequence modeling, or attention mechanisms applied to biological sequences.
• Familiarity with multi-omic data integration methods.
• Background in quantitative genetics or genomic prediction.
• Experience with functional genomics data.
• 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.
• Exposure to classical ML approaches.
• Experience with model deployment in production.
• Track record of interdisciplinary collaboration with experimental biologists.

Benefits

• Salary of approximately $110k-150k.
• Additional compensation may include a bonus or incentive program.
• Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.

Company Description

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).