Machine Learning Researcher, Genomic AI

Responsibilities

• Genomic & Omic Model Development: Design, train, and evaluate deep learning models (including large language models, transformers, and representation learning architectures) on diverse omic datasets - whole-genome sequences, gene expression profiles (RNA-seq), epigenomic marks, k-mer spectra, skim-seq, pangenome graphs, and multi-omic integrations.
• Genomic Language Models: Develop and fine-tune foundation models for DNA/RNA sequences that capture long-range dependencies, regulatory grammar, and evolutionary conservation to predict variant effects, gene function, and trait associations in crop genomes.
• Genomic Selection & Editing Enablement: Build predictive models that connect genotype to phenotype across environments, identify high-value editing targets, and rank candidate genetic interventions with biological interpretability and statistical rigor.
• Functional Data Integration: Integrate heterogeneous biological data types-including high-resolution genome assemblies, structural variants, gene regulatory networks, protein structure predictions, and phenomic measurements-into unified predictive frameworks.
• Interdisciplinary Collaboration: Work closely with molecular biologists, geneticists, breeders, bioinformaticians, and computational scientists to ground models in biological reality, design informative training data strategies, and validate predictions experimentally.
• Scalable Deployment: Partner with engineering and IT teams to operationalize models within genomic selection pipelines, editing nomination workflows, and decision-support platforms used by breeding programs globally.
• Research Contribution: Advance the state of the art through publications, internal seminars, and engagement with the broader computational biology and AI research community.
• Documentation & Communication: Communicate complex modeling results to diverse audiences, prepare technical reports, and build organizational confidence in AI-driven biological discovery.

Qualifications

• PhD in Computational Biology / Bioinformatics, Machine Learning / Deep Learning, Genomics / Statistical Genetics, Computer Science (with focus on biological or sequential data), Biostatistics / Quantitative Genetics, Systems Biology, or another 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 communication skills and ability to collaborate effectively across disciplines.

Benefits

• Paid 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.