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Anduril Industries, a defense technology company, is seeking a Staff Materials Engineer to establish and own the design allowables and materials data infrastructure across the company's diverse product portfolio—from autonomous aircraft and submarines to missiles and solid rocket motors.
In this role, you will define how materials characterization and test data transform into defensible, analysis-ready design allowables company-wide. You'll architect the methodology, infrastructure, and governance that enables materials engineering to scale across business lines.
Key responsibilities include:
• Own the Design Allowables Standard: Define statistical basis (A-basis/B-basis, pooling, reduction methods), acceptance criteria, traceability, versioning, and controlled release of design values.
• Build the Materials Data Pipeline: Architect an enterprise materials database that ingests, structures, governs, and releases materials data. Enable advanced and non-standard materials to be characterized once and reused across programs.
• Transform Testing into Strategic Workflow: Convert ad-hoc materials characterization into a defined system specifying what gets tested, to what rigor, and how results become trusted allowables.
• Establish Fatigue and Fracture-Critical Methodology: Develop standardized approaches to fatigue-life assessment and damage tolerance, ensuring high-consequence design decisions rest on consistent, defensible foundations.
• Extract Value from Lifecycle Data: Build systems to turn supplier, process, and lot-to-lot variability data into a growing statistical foundation and queryable digital thread of material traceability.
• Serve as Cross-Business-Line Technical Authority: Be the SME for materials allowables across the portfolio, partnering with design, analysis, test, and quality teams.
• Seed the Function: Set technical standards for materials data at Anduril and build the team and capability around you.
You thrive in ambiguity, measure impact by how much better you make engineers around you, have a bias for action, and think in systems. You're comfortable with both statistical rigor and data architecture, and you excel at bringing structure to loosely-defined, fast-moving problems.
