Winter 2027 Library Science Co-op

ABOUT THE TEAM

Anduril's Maritime Division is transforming American sea power to meet the defining national security challenges of our era and secure our future of maritime dominance.

We are scaling at an historic pace, anchored by Arsenal-2, our new software-defined shipyard being built in Baltimore County, Maryland. It's part of a new $6.6 billion investment in America's submarine industrial base.

Arsenal-2 is purpose-built to break long-standing industrial bottlenecks and scale production of critical components for the Navy's Virginia-class attack submarines to support fleet requirements. At the same time, it will build the industrial muscle to field our next-generation AUVs at scale.

We aren't waiting for the new shipyard to be completed. The work on this transformation starts right now, with development inside our new 160,000 square foot Proto-Factory in Orange County, California, where the team is already laying the foundation. As the prototyping hub for Arsenal-2, this facility gives us a multi-year head start, proving out the advanced manufacturing methods, digital workflows, and quality systems that will scale across our broader shipyard network.

In Anduril's Maritime Division, we are actively building America's next decisive advantage at sea, and this is the moment to join us.

ABOUT THE JOB

Maritime Digital Production builds the digital systems behind Anduril's maritime build chain: data pipelines, contextualization layers, workflow and execution tools, applied intelligence, and the interfaces between OT and IT. Everything we build has to survive production constraints, which means real crews, real equipment, and real disruption.

Raw data off the factory floor doesn't mean much on its own. A tag reading PLC4_TT_1142 tells you nothing until someone establishes that it's a temperature sensor, on a specific machine, in a specific cell, measuring a specific process step. Doing that for thousands of signals is a cataloging problem before it's an engineering one.

That's the job. You'll build the vocabularies, hierarchies, and metadata standards that give factory data meaning, working alongside data engineers and manufacturing engineers who need the structure and don't have the training to build it.
If you like building a taxonomy and want to see one used somewhere other than a library, this is that.

WHAT YOU'LL DO

• Develop and maintain controlled vocabularies for equipment, materials, processes, and locations across the build chain.
• Build asset hierarchies and naming conventions that structure plant-floor data into a consistent model.
• Write and apply metadata schemas for industrial data sources, documenting what each field means and how it should be used.
• Catalog data sources and maintain entries in the team's data catalog, including provenance and ownership.
• Audit existing tags and data models for inconsistency and gaps and propose corrections.
• Document standards and authoring guidelines so naming decisions stay consistent after you leave.
• Work with data and manufacturing engineers to check that the models match how work actually happens on the floor.
• Present your work to the team at the end of the session.

REQUIRED QUALIFICATIONS

• Must be pursuing a bachelor’s or master’s degree in Library Science, Information Science, Knowledge Management, Archival Studies, Linguistics, or a related field.
• Must be returning to school at the end of the internship to continue academic studies for at least one quarter or semester.
• Must be a U.S. Person due to required access to U.S. export-controlled information or facilities; U.S. clearance eligibility may be required depending on program.
• Ability to complete a 16 to 24-week in-person co-op in one of our offices in winter 2027.

PREFERRED QUALIFICATIONS

• Coursework or project experience in metadata, cataloging, taxonomy, controlled vocabularies, or classification systems.
• Attention to detail, and patience for working through ambiguity toward a consistent structure.
• Clear writing, particularly for documentation meant to outlast its author.

• Familiarity with metadata standards and modeling approaches (Dublin Core, SKOS, RDF, OWL, or similar).
• Exposure to data catalog tooling, knowledge graphs, or ontology editors such as Protégé.
• Basic scripting in Python or SQL for inspecting and transforming data.
• Interest in manufacturing and the physical systems behind the data.
• Coursework in digital curation, data governance, or information architecture.