Sr Staff Data Scientist, Virtual Biology Initiative, AI Research

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 Initiativea $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 groundedaccelerating 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 typesincluding imaging, sequencing, and multimodal datathat 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 scientific goals
• Identify and drive new data acquisition and generation opportunities, from consortium partnerships to internal experimental pipelines
• Serve as a technical mentor and leader, raising the bar for data science and ML rigor across the organization

What You'll Bring

• 12+ years of experience (or PhD + 7 years) working with large-scale biological datasets, including ownership of end-to-end data products
• Deep expertise in at least one of: (a) imaging datamicroscopy, cell phenotyping, spatial biology, and the data characteristics of image-based biological measurement; or (b) genomics databulk and single-cell sequencing, functional genomics, epigenomics, transcriptomics, spatial biology, and/or multi-omics
• Understanding of how to transform raw biological data into AI-ready datasets, including familiarity with scientific best practices, noise characteristics, batch effects, and quality assessment specific to your domain
• Experience with tokenization strategies for non-text data (images, sequences, graphs, time series) or with creating data representations and feature engineering for machine learning in scientific or biological contexts
• Strong expertise in data science and statistical modeling; familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning
• Strong computational skills; demonstrated ability to design robust, extensible data architectures
• Excellent communication and leadership skills, with the ability to translate between biology, ML, and engineering audiences and align teams to deliver complex projects
• Creative, first-principles thinking about how to structure data for learning

Compensation

The Redwood City, CA  &  New York City, NY  base pay range for a new hire in this role is $241,000.00 - $331,100.00. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. 

Better Together

As we grow, were excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the teams manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.

Benefits for the Whole You 

Were thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If youre interested in a role but your previous experience doesnt perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

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