Data Engineer

Biohub is a 501(c)(3) biomedical research organization building the first large-scale scientific initiative combining frontier AI with frontier biology to solve disease. We build the technology to help scientists around the world use AI-powered biology to study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it. With our compute capacity, AI research and engineering, and state-of-the-art technology for measuring, imaging, and programming biology, we are enabling scientists worldwide to use AI-powered biology to advance our understanding of human health. The Team Biohub is a 501(c)(3) biomedical research organization building the first large-scale scientific initiative combining frontier AI with frontier biology to solve disease. We build the technology to help scientists around the world use AI-powered biology to study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it. With our compute capacity, AI research and engineering, and state-of-the-art technology for measuring, imaging, and programming biology, we are enabling scientists worldwide to use AI-powered biology to advance our understanding of human health. The Opportunity The role is part of the Data Engineering 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, 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. As a data engineer at Biohub, you'll be designing systems that ingest data from public repositories, transform heterogeneous biological formats into AI-ready datasets, combine that with proprietary datasets, and deliver training datasets to researchers pushing the boundaries of what's possible in biological AI. The infrastructure you build will directly shape what our models can learn. We're a small team with significant resources and long time horizons. We use AI tools aggressively in our own work—Claude Code, agents for workflow automation, LLMs for metadata extraction. We care about code quality, operational reliability, and building systems that scale. And we care about the biology: we want engineers who can recognize when a pipeline output is technically correct but scientifically wrong. If you want to work at the intersection of large-scale infrastructure and frontier science, with real autonomy and the chance to build something genuinely new, we'd like to talk. What You'll Do Design and build data pipelines that process genomic and imaging data at petabyte scale Solve performance and bandwidth challenges with creative engineering Build agent-based systems for automated dataset curation, quality control, and workflow generation Create tooling for data cataloging and registration that makes datasets discoverable and accessible Collaborate with AI Research teams to translate model requirements into data specifications, and with our scientists to integrate public and internal data into large-scale ai-ready datasets Improve pipeline reliability and observability, working toward 99%+ success rates without manual intervention What You'll Bring 5+ years experience building reliable, operable data systems at scale (100s terabytes to petabytes) Strong software engineering fundamentals Experience deploying distributed computing frameworks like Databricks, Spark, or Ray for large-scale data processing Experience with cloud infrastructure (AWS preferred) and HPC environme ... (truncated, view full listing at source)