Machine Learning Researcher, Genomic AI

Machine Learning Researcher, Genomic AI

We are seeking a Machine Learning Researcher with expertise in machine learning for biological systems, with a particular focus on 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.

• Genomic & Omic Model Development:
  Design, train, and evaluate deep learning models on diverse omic datasets, including 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.

• PhD in one of the following or closely related fields:
  • 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 Tensor Flow) 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.

• Hands‑on experience developing or fine‑tuning genomic language models or biological foundation models (e.g., GPN, Plant Caduceus, Nucleotide Transformer, Evo, Enformer, Alpha Genome).
• Experience with transformer architectures, long‑context sequence modeling, or attention mechanisms applied to biological sequences.
• Familiarity with multi‑omic data integration methods (e.g., multi‑modal autoencoders, contrastive learning across modalities, graph neural networks on biological networks).
• Background in quantitative genetics or genomic prediction and understanding of breeding program workflows.
• Experience with functional genomics data: ATAC‑seq, ChIP‑seq, Hi‑C, single‑cell transcriptomics, or CRISPR screen data.
• Knowledge of pan genomics, structural variant calling, or comparative genomics across crop species.
• Experience with…