Sr Staff Data Scientist, Virtual Biology Initiative

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.

The Team 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. The Opportunity 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. 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.

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 translate that thinking into data representations that enable novel training architectures.

You'll work directly with scientists, computational biologists, data engineers, and AI researchers to define model input and biological evaluations. You will operate with broad scope and high autonomy, influencing roadmap decisions across teams while mentoring senior individual contributors. Success in this role means creating and implementing data systems that are not only large, but adaptive, interpretable, and scientifically grounded—accelerating progress toward robust biological frontier models and ultimately advancing human health.

What You'Ll Do

Set technical vision and strategy for the design of data representations and tokenization strategies across biological data types—including imaging, sequencing, and multimodal data—that enable novel model architectures Develop, deploy and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects Evaluate model performance, identifying which biological signals are captured or lost and iterating to improve Partner deeply with ML engineers and AI researchers to co-design datasets and optimize model training, evaluation, and generalization Lead cross-functional initiatives spanning data engineering, infrastructure, science, and product, aligning technical execution with long-term sci