Staff Data Scientist, Genomics

About this role

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

Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology — developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide.

Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools.

The Opportunity

This is an opportunity to shape the future of biological research by pushing the boundaries of what AI can achieve in science. You’ll work alongside leading experts in AI and biology, with the resources and mandate to tackle some of the most important questions in human health — advancing frontier AI research, accelerating engineering velocity, connecting rich biological data to AI systems, enabling reliable compute across environments, and translating models and data into usable, scalable applications that drive scientific impact.

The role is part of the Data team, which focuses on owning the strategy, sourcing and implementation for data supporting AI research and development. We're a small team with significant resources and long time horizons. Our goal is to maximize the speed, agility, and capability of biological AI research by connecting public data resources and Biohub's experimental platforms to AI systems. The data that trains biological frontier models comes in dozens of modalities (sequences, images, spatial coordinates, time series, molecular structures, metadata, publication artifacts) each with its own noise characteristics, biases, and information content. The question of how to represent this data for learning is one of the most important open problems in biological AI.

We’re looking for a data scientist with deep expertise in genomics (e.g., bulk and single-cell sequencing, functional genomics, CRISPR screens), who thinks creatively about data representation and tokenization, and can translate that thinking into novel training architectures. You’ll work across experimental, computational, and AI teams to build scalable, interpretable genomic data systems that power next-generation biological models and accelerate human health discovery. You will operate with broad scope and high autonomy, influencing roadmap decisions across teams, and mentoring senior individual contributors. Success in this role means scaling 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 for diverse biological data types that enable novel model architectures.

• Define data standards and quality metrics that enable reliable cross-dataset integration and model-ready data products.

• Develop and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects

• Evaluate how representation choices impact 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 feedback loops that 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 external collaborations 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

• PhD in computational biology, bioinformatics, or a quantitative biological field

• 8+ years of experience working with large-scale biological datasets (genomics, epigenomics, transcriptomics, proteomics, or multi-omics), including ownership of end-to-end data products.

• Deep understanding of biological measurement types (sequencing, imaging, proteomics, or related), their underlying data characteristics, and how to transform raw data into ai-ready datasets.

• Experience designing data representations or feature engineering for machine learning in biomedical contexts

• Strong computational skills (Python, scientific computing libraries); Demonstrated ability to design robust, extensible data architectures and to evolve standards in fast-moving scientific domains.

• Strong expertise in machine learning and statistical modeling, with experience applying these methods to data quality assessment, automation, or decision-making systems. Familiarity with modern ML architectures (transformers, diffusion mod

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