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.
Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning
We are seeking a Sr. Machine Learning Researcher with strong expertise in the mathematical foundations of machine learning and scientific computing to develop next-generation domain-aware models for agriculture. This role sits at the intersection of applied mathematics, domain-aware modeling, and deep learning, with the goal of building models that respect and encode the underlying structure of biological and environmental systems. You will design principled, interpretable, and generalizable AI architectures that integrate scientific knowledge from genetics to crop physiology to environmental dynamics- into data-driven frameworks. Your work will directly enable transformative applications in genomic selection and genome editing target identification, accelerating the development of improved crop varieties worldwide.
YOUR TASKS AND RESPONSIBILITIES
The primary responsibilities of this role are:
Scientific ML Model Development: Design, build, and validate domain-aware machine learning models (e.g., biology-informed, and hybrid mechanistic-statistical architectures) that incorporate prior scientific knowledge into learning algorithms for agricultural and genomic applications.
Mathematical Framework Design: Develop novel architectures and loss functions that embed biological constraints, conservation laws, symmetry properties, or known functional relationships into neural network training, ensuring physically and biologically consistent predictions.
Genomic Selection & Editing Enablement: Architect models that leverage high-dimensional genomic, phenomic, and environmental data to predict complex trait outcomes, identify causal genetic variants, and prioritize genome editing targets with quantified uncertainty.
Uncertainty Quantification: Implement rigorous uncertainty quantification frameworks (Bayesian deep learning, ensemble methods, probabilistic surrogate models) to provide decision-makers with calibrated confidence estimates on model predictions.
Interdisciplinary Collaboration: Partner with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate domain expertise into model architecture decisions and validate model outputs against biological ground truth.
Scalable Deployment: Work with engineering and IT teams to transition research prototypes into production-grade models integrated within breeding and discovery pipelines, ensuring reproducibility, scalability, and maintainability.
Documentation & Communication: Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organizational trust in AI-driven decision-making.
Bayer seeks an incumbent who possesses the following:
Demonstrated research output (publications, thesis work, or applied projects) in scientific machine learning, numerical methods for differential equations, or data-driven modeling of physical/biological systems.
Experience with Bayesian inference, Gaussian processes, hierarchical models, or probabilistic programming.
Exposure to genomics data structures (e.g., variant matrices, linkage disequilibrium, population genetics) or quantitative genetics (e.g., genomic BLUP, marker-effect models) - not required, but valued.
Familiarity with ensemble methods, gradient-boosted models, kernel methods, or classical statistical learning as complementary tools.
Employees can expect to be paid a salary of approximately $120k-170k. 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 6/26/26.
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
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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| Bayer is an E-Verify Employer. | |
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| Location: | United States : Residence Based : Residence Based || United States : Missouri : Creve Coeur | |
| Division: | Crop Science | |
| Reference Code: | 871164 | | |
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| Contact Us |
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| Email: | hrop_usa@bayer.com |
