Staff Data Scientist, Imaging

Biohub•Redwood City, CA

5d•$214,000 - $294,800•Hybrid

About The Position

Biohub is a large-scale initiative focused on accelerating scientific discovery by integrating frontier AI models, massive compute, and experimental capabilities. The AI research team at Biohub leverages state-of-the-art AI to drive insights in biology, developing novel AI models, engineering robust systems, and translating advances into practical tools for researchers. The approach is comprehensive, combining AI model development, engineering talent, biological data, computing infrastructure, and strategic partnerships. Success relies on training AI models for biology, building efficient engineering systems, executing a data strategy, operating an AI compute platform, and creating impactful scientific tools. This role is within the Data team, responsible for the strategy, sourcing, and implementation of data supporting AI research and development. The team aims 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 used for training biological frontier models includes various modalities like sequences, images, spatial coordinates, time series, molecular structures, metadata, and scientific literature, each with unique characteristics. Representing this data for learning is a key challenge. The Staff Data Scientist will operate with significant autonomy, influencing roadmap decisions and mentoring senior individual contributors. The goal is to scale data systems that are adaptive, interpretable, and scientifically grounded, accelerating progress toward robust biological frontier models and advancing human health. This role is for individuals who understand biological measurement, think creatively about data representations and tokenization, and can translate these ideas into novel training architectures. Collaboration with experimental and computational scientists, data scientists, AI researchers, and data engineers is essential to define model inputs and ensure scalability.

Requirements

PhD in computational biology, bioinformatics, or a quantitative biological field
Experience with tokenization strategies for non-text data (images, sequences, graphs, time series)
Track record of novel methodological contributions (publications, open-source tools, or production systems)
Familiarity with biological foundation models (ESM, scGPT, or similar)
Deep understanding of imaging data, their underlying data characteristics, and how to transform raw data into ai-ready datasets.
Experience designing data representations or feature engineering for machine learning, ideally in scientific or biological contexts
Familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning
Strong computational skills (Python, scientific computing libraries); comfort working with large-scale datasets
Creative, first-principles thinking about how to structure data for learning

Responsibilities

Design data representations and tokenization strategies for imaging data that enable novel model architectures
Coordinate Experimental, Data Science, Data Engineering and AI Research teams to translate biological structure into learnable representations—defining priorities and appropriate structures for metadata and data that information models can access and consume
Work across those teams to guide data acquisition priorities, define quality criteria, and assess external datasets from a representation perspective
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