Bio-Rad is a global leader in developing and manufacturing a wide range of products for the life science research and clinical diagnostic markets.
About the role
Bio-Rad's Clinical Diagnostics Group is building the computational foundation for the next generation of molecular diagnostics, bringing together data across multiple assay modalities to support biomarker discovery and diagnostic development. This role owns the pipeline development and machine learning method design behind that effort.
What they're looking for
- Education: M.S. or Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related technical field
- Pipeline and Infrastructure: Demonstrated experience building production workflows in Nextflow or Snakemake with Docker or Singularity containerization, deployed on AWS (S3, EC2, Batch) or GCP (Dataproc, Cloud Storage)
- Machine Learning and Integration: Supervised and unsupervised methods plus practical deep learning experience on biological data, with exposure to multiomics integration and cross-modal feature alignment
- Software Development: Fluency in Python (PyTorch, scikit-learn, pandas) and R (Bioconductor, Seurat, ggplot2), with strong engineering practice in version control, code review, testing, and documentation
More about this role
Bio-Rad's Clinical Diagnostics Group is building the computational foundation for the next generation of molecular diagnostics, bringing together data across multiple assay modalities to support biomarker discovery and diagnostic development. This role owns the pipeline development and machine learning method design behind that effort. It sits within a team covering statistics, computational biology, data science, and software engineering, and is the primary technical owner for bioinformatics infrastructure and ML-driven discovery work. The role will be filled at either the Scientist or Senior Scientist level depending on the experience of the candidate, with the Senior level carrying broader ownership of technical direction and mentorship within the team.
- Design and build reproducible, containerized analysis pipelines for high-dimensional biological data on cloud infrastructure, and establish the reproducibility and CI/CD standards the team builds against.
- Develop machine learning methods for biomarker discovery, including supervised, unsupervised, and deep learning approaches applied to biological data.
- Lead multi-omic integration work, aligning features across assay...
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