Staff Data Scientist, Reasoning

About the role

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 is responsible for maximizing 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 AI can reason across these diverse descriptions of biology to answer specific experimental questions is one of the core challenges in biology. You will define the data approach to train our reasoning system. To do so, you will operate with broad scope and high autonomy, influencing roadmap decisions across teams while mentoring 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 scientific experimentation deeply, think creatively about data representations and tokenization strategies, and have experience building reasoning systems. 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.

Responsibilities

• Design reasoning tasks for our models.
• Build training datasets that capture biology experiments, including experimental design, hypothesis generation, evidence interpretation, and scientific inference
• Design training strategies that teach reasoning capabilities, working closely with AI Research to translate data approaches into model behavior
• Create evaluation and benchmarking frameworks that measure reasoning quality and analyze model behavior to influence the next set of evaluations, environments, and data.
• Partner with Scientific Data Strategy and Data Engineering to identify and acquire source materials (literature, protocols, experimental records) that contain reasoning signal
• Set technical direction for reasoning data efforts, influencing priorities and mentoring other data scientists working in this area

Requirements

• PhD in machine learning, computational biology, or another quantitative field
• Hands on understanding of how scientists reason across diverse experimental systems as obtained from hands-on experience with laboratory science in biology, biochemistry, or chemistry
• Experience curating or creating training data and tokenization strategies fo

Benefits

Additional Information

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 sciences and 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 biological and 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.

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