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

Bayer; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

Bayer seeks a Sr. Machine Learning Researcher to develop domain-aware models for agriculture, fusing biology, genomics and environmental data into interpretable AI. This role advances genomic selection and genome editing in a global crop science context.

It combines applied mathematics, ML theory, and production deployment, requiring collaboration with geneticists, plant biologists, agronomists and software engineers to translate research into scalable pipelines.

Qualifications

• PhD in ML or related quantitative field with strong modeling depth.

Responsibilities

• Scientific ML model development integrating domain knowledge into learning algorithms.

Skills

Deep learning

Scientific computing

Genomics data

PyTorch

JAX

TensorFlow

Bayesian methods

Interdisciplinary collaboration

Education

PhD in Machine Learning

Tools

PyTorch

JAX

TensorFlow

Job description

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

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.

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 combines applied mathematics, domain‑aware modeling, and deep learning to build models that encode the underlying structure of biological and environmental systems. You will design interpretable, generalizable AI architectures that integrate scientific knowledge - from genetics to crop physiology to environmental dynamics - into data‑driven frameworks. This work directly enables genomic selection and genome editing target identification , accelerating the development of improved crop varieties worldwide.

Your Tasks And Responsibilities

• Scientific ML Model Development: Design, build, and validate domain‑aware machine learning models (e.g., biology-informed, 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 to ensure 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.

Who You Are

• PhD in Machine Learning / Deep Learning, Applied Mathematics, Computational Science & Engineering, Physics, Statistics / Probabilistic Modeling, Computer Science (with scientific computing or numerical methods focus), or a 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.
• Familiarity with Agentic AI‑related skills including building agentic AI systems, tools, and multi‑agent architectures
• 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:
• Physics‑Informed Neural Networks (PINNs)
• Biology‑Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs)
• Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs)
• Operator learning methods (e.g., DeepONet, Fourier Neural Operator)
• 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.

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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Bayer is an E-Verify Employer.

Location

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

Division

Crop Science

Reference Code

871164

Contact Us

Email: [e-mail]