Job description

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 role is part of the Data team, which focuses on owning the strategy, sourcing and implementation for data supporting AI research and development. 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, preprints and published papers—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. You will operate with broad scope and high autonomy, influencing roadmap decisions across teams while 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. We're looking for data scientists who can work at this frontier: people who understand biological measurement deeply, think creatively about data representations and tokenization strategies, and can translate that thinking into novel training architectures. You'll work directly with experimental and computational scientists, data scientists and AI researchers to define what the models see and how they see it, and data engineers to make this work at scale. This is a role for someone who wants to invent the methods that make biological frontier models possible. What You'll Do 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 What You'll Bring 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 Compensation The Redwood City, CA base pay range for a new hire in this role is $214,000 - $294,800 for staff-leveled candidates and $241,000 - $331,1000 for senior staff candidates. 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. This position may be eligible to participate in our discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with our total rewards philosophy and may vary by role. Better Together As we grow, we’re 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 team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process. Benefits for the Whole You We’re 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 you’re interested in a role but your previous experience doesn’t 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. #LI-Hybrid

Requirements

Must-haves

Design data representations and tokenization strategies for imaging data that enable novel model architectures
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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpython

Platform: greenhouse
Effort: quick

Benefits

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401(K)

Paid Time Off