# Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning **Company**: Bayer **Location**: Creve Coeur, Missouri **Experience**: senior **Job type**: full-time **Salary**: $120k-170k **Category**: IT **Industry**: Life Sciences **Wikidata**: https://www.wikidata.org/wiki/Q152051 **Apply**: https://talent.bayer.com/careers/job/562949977361395?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_e9b8a707-bb1 ## Description 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 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. **Responsibilities:** - Scientific ML Model Development: Design, build, and validate domain-aware machine learning models 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. - 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 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. - 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 organisational trust in AI-driven decision-making. **Requirements:** - PhD in a related quantitative discipline with demonstrated depth in mathematical modeling. - Demonstrated research output in scientific machine learning, numerical methods for differential equations, or data-driven modeling of physical/biological systems. - Proficiency in modern deep learning frameworks 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 Qualifications:** - 5+ years post-PhD relevant experience. - Demonstrated experience with domain-aware modeling paradigms. - 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 or quantitative genetics. - Experience deploying ML models into production environments. - Experience collaborating in interdisciplinary research teams. **Compensation:** - Salary: approximately $120k-170k. - Additional compensation may include a bonus or incentive program. - Benefits include health care, vision, dental, retirement, PTO, sick leave, etc. ## Skills ### Required - Machine Learning - Deep Learning - Applied Mathematics - Computational Science - Scientific Computing - Python - PyTorch - TensorFlow ### Nice to have - Physics-Informed Neural Networks - Biology-Informed Neural Networks - Neural Ordinary Differential Equations - Operator learning methods - Bayesian inference - Gaussian processes - hierarchical models - probabilistic programming --- Source: [Apply at talent.bayer.com](https://talent.bayer.com/careers/job/562949977361395?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)