About the role
<div class="content-intro"><p>Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.</p></div><h2><span style="color: rgb(40, 40, 39); font-family: helvetica, arial, sans-serif;">The Team</span></h2> <p>Biohub's data organization is responsible for producing biologically informative, petabyte-scale, AI-ready datasets for frontier models of cell biology. Our work spans genomics, imaging, and proteomics, and we're building the data systems that will enable a new generation of biological AI. The team consists of data engineering, data science, and technical program management. We operate with a flat structure that emphasizes strong IC ownership. We're solving hard problems at the intersection of scientific strategy, large-scale data infrastructure, and foundation model training.</p> <h2><span style="color: rgb(40, 40, 39); font-family: helvetica, arial, sans-serif;">The Opportunity</span></h2> <p>In April 2026, Biohub launched the Virtual Biology Initiative—a $500 million, five-year commitment to galvanize a global effort to build predictive models of the human cell. This initiative will bring together leading institutions to generate the multi-modal biological data, at unprecedented scale, that will power the next generation of AI models for biology while producing datasets of unprecedented size.</p> <p>Our data science team defines the algorithms and processing approaches that turn raw biological measurements into rich representations models can actually learn from. That includes designing data formats and representations optimized for AI use cases, building cost-aware processing pipelines that balance expressiveness with efficiency, developing scalable QC and validation frameworks across modalities, creating agent-augmented curation tools for metadata extraction and ontology mapping, and building the cross-modal entity resolution and semantic infrastructure that ties it all together.&nbsp;</p> <p>Both the scale and domain are active research areas. How do you tokenize a cell image? How do you represent a perturbation experiment? How do you combine transcriptomics with imaging in a way that preserves biological meaning? These questions don't have established answers. We need scientific leaders who can work at this frontier: people who understand biological measurement deeply, think creatively about data representations, sampling, and tokenization strategies, and can transl