Data Infrastructure Engineer

What if… you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies in order to transform health care and sustainability?

About Expedition:

Expedition Medicines is a privately held, early-stage biotechnology company pioneering the emerging field of Protein Editing. At Expedition Medicines we create small molecules that edit protein structure and function to unlock presently undruggable targets and a broad array of therapeutic modalities. Our platform integrates novel small molecule chemistry and chemoproteomic discovery technologies with Machine Learning (ML) to enable generative design. Expedition Medicines is backed by Flagship Pioneering, bringing their courage, vision, and resources to guide Expedition Medicines from platform validation to patient impact. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us! 

Position Summary: 

We are seeking a highly skilled, hands-on Senior / Principal Data Infrastructure Engineer to design, build, and own the data systems that make our proteomics data discoverable, trustworthy, and usable across the organization. Expedition Medicines develops machine learning tools trained on proteomics data generated by our experimental platform, and this role is critical to the robust, scalable collection, management, and analysis of that data at scale.

This is a senior individual-contributor role for an engineer who writes the code, ships the systems, and sets the technical direction. You will work independently, with a clear point of view on where our data infrastructure needs to go, and you will turn that vision into working, production-grade systems that ML scientists, medicinal chemists, and biology teams rely on every day.

Responsibilities:

• Architect and build: Design and implement the core data platform that integrates experimental proteomics data with computational tools, with high availability, scalability, and security. Write production code.
• Own the technical vision: Define and drive the technical roadmap for data infrastructure, identifying the highest-leverage problems.
• Automate and integrate: Build automated workflows across proteomics research environments, including high-throughput assays and mass spectrometry data processing. Integrate instruments, pipelines, and LIMS (e.g., Dotmatics) for seamless, traceable data capture.
• Data pipelines: Develop and operate robust data pipelines using workflow orchestration tools (e.g., Flyte), and design layered (e.g., medallion-style) data architectures turning raw instrument output into ML-ready datasets reliably and reproducibly.