机器学习研究员，基因组人工智能（驻地，美国）

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 Machine Learning Researcher with expertise in machine learning for biological systems, particularly 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. You will develop models that learn the grammar of genomes, predict functional consequences of genetic variation, and connect molecular signatures to whole-organism phenotypes across diverse crop species. This work directly supports genomic selection and genome editing target identification, turning sequence-level intelligence into breeding and discovery decisions at a global scale.

Genomic & Omic Model Development: Design, train, and evaluate deep learning models (LLMs, transformers, and representation learning architectures) on whole-genome sequences, gene expression profiles, epigenomic marks, k-mer spectra, skim-seq, pangenome graphs, and multi-omic integrations.

Genomic Selection & Editing Enablement: Build predictive models connecting genotype to phenotype across environments, identify high-value editing targets, and rank candidate genetic interventions with biological interpretability and statistical rigor.

Documentation & Communication: Communicate complex modeling results to diverse audiences, prepare technical reports, and build organizational confidence in AI-driven biological discovery.

PhD in Computational Biology / Bioinformatics, Genomics / Statistical Genetics, Machine Learning / Deep Learning, Computer Science (with focus on biological or sequential data), Biostatistics / Quantitative Genetics, Systems Biology, or a closely 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) and familiarity with large-scale model training (distributed training, GPU clusters).

Working knowledge of molecular biology fundamentals sufficient to interpret model outputs in biological context (e.g., gene regulation, variant consequence, population genetics).

Strong written/verbal communication and cross-disciplinary collaboration skills.

Hands-on experience developing or fine-tuning genomic language models or biological foundation models (e.g., GPN, PlantCaduceus, Nucleotide Transformer, Evo, Enformer, AlphaGenome or similar large-scale sequence architectures for genomic prediction and functional track prediction).

Experience with functional genomics data: ATAC-seq, ChIP-seq, Hi-C, single-cell transcriptomics, or CRISPR screen data.

Background in quantitative genetics or genomic prediction (e.g., GBLUP, Bayesian alphabet models, marker-effect estimation) and understanding of breeding program workflows.

Familiarity with multi-omic data integration methods (e.g., multi-modal autoencoders, contrastive learning across modalities, graph neural networks on biological networks).

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 (attention visualization, in-silico mutagenesis, feature attribution) to derive biological hypotheses from model internals.

Exposure to classical ML approaches (gradient-boosted methods, kernel methods, Gaussian processes) as complementary or baseline tools.

Experience with model deployment in production (MLOps pipelines, containerization, API development, cloud/HPC infrastructure).

Track record of interdisciplinary collaboration with experimental biologists, resulting in validated biological predictions.

Employees can expect to be paid a salary of approximately $110k-150k. 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.

YOUR APPLICATIONBayer 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 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.