Vivodyne generates human data at AI scale with autonomous labs testing the most realistic human tissues ever grown outside the body. Backed by Khosla.
About the role
We are expanding our capabilities to generate high-throughput -omics data across automated human tissue models, with a primary focus on single-cell and bulk transcriptomics, secretomics, and proteomics. We're looking for a Bioinformatician who can develop new analytical methods and build robust infrastructure to support them at scale.
What they're looking for
- Experience with multi-modal data integration (e.g., combining imaging, transcriptomics, and proteomics features)
- Background in drug discovery, toxicology, or translational research where multi-omic data inform therapeutic decisions
- Experience with representation learning or ML for biological data (embeddings for cells, genes, or proteins), especially in collaboration with ML specialists
- Familiarity with organoids, primary tissues, or organ-on-chip systems
More about this role
We accelerate the successful discovery, design, and development of human therapeutics by testing on large, lab-grown human organ tissues at massive scale, driving technological advancement at the convergence of novel biology, robotics, and AI. We identify and validate new therapeutic targets and de-risk new therapeutic assets by producing clinically translatable multi-omic data from our proprietary, physiologically-realistic human organ tissues at unprecedented scale, speed, and quality. This enables us to produce more human data than all clinical trials in the U.S. combined. We’re financially backed by some of the most selective and successful venture funds, and we have already partnered with a majority of the top 10 multinational pharmaceutical companies to discover and develop better, safer drugs and dramatically reduce the burden of animal testing.
www.vivodyne.com
The Software & Data Science team at Vivodyne tackles some of the hardest problems at the intersection of human biology, large-scale data, and machine learning using uniquely rich imaging and multi-omics datasets from lab-grown human tissues.
We are expanding our capabilities to generate high-throughput -omics data...
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