Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning

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.

• Research Contribution: Contribute to publications in leading venues, participate in the internal scientific community, and stay at the frontier of scientific machine learning methodology.

• Documentation & Communication: Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organizational trust in AI-driven decision-making.

WHO YOU ARE

Bayer seeks an incumbent who possesses the following:

Required:

• PhD in one of the following or closely related fields

• Machine Learning / Deep Learning

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• Applied Mathematics

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• Computational Science & Engineering

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• Physics

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• Chemical, Mechanical, or Biomedical Engineering

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• Computer Science (with scientific computing or numerical methods focus)

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• Statistics / Probabilistic Modeling

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• Another related quantitative discipline with demonstrated depth in mathematical modeling

• 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.

• Proficiency in modern deep learning frameworks (PyTorch, JAX, or TensorFlow) and scientific computing libraries.

• Experience formulating and solving problems involving high-dimensional, structured, or multi-modal data.

• Strong communication skills and willingness to collaborate across disciplines.

Preferred:

• 5+ years post-PhD relevant experience

• Demonstrated experience with one or more of the following domain-aware modeling paradigms:

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• Physics-Informed Neural Networks (PINNs)

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• Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs)

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• Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs)

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• Operator learning methods (e.g., DeepONet, Fourier Neural Operator)

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• Hybrid mechanistic-data-driven models

• Experience with Bayesian inference, Gaussian processes, hierarchical models, or probabilistic programming.

• Familiarity with nonlinear dynamics, dynamical systems theory, or systems biology modeling.

• Background in surrogate modeling, model reduction, or multi-fidelity methods.

• 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.

• Experience deploying ML models into production environments (MLOps, containerization, cloud-based HPC).

• Experience collaborating in interdisciplinary research teams spanning experimental and computational scientists.

• 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., 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.

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., 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. Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.