Data Engineer

About the role

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.