AI Systems Engineer

<div class="content-intro"><p>Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.</p></div><h3>Team Overview</h3> <p>Anduril’s Technical Publications organization supports complex hardware, software, autonomy, and&nbsp;defense systems across multiple divisions, business lines, and programs. The Product Manager, Technical&nbsp;Writing Systems, owns the authoring and publishing platform. This role builds the AI systems that extend&nbsp;that platform so the documentation pipeline handles more work at lower cost and lower latency.</p> <h3>Role Specifics</h3> <p>As the Applied LLM Systems Engineer, your mission is to design, build, and operate production AI systems that improve how technical documentation is created, transformed, validated, maintained, and delivered. You will build and maintain AI-assisted authoring, automated publishing, and multi-agent coordination tooling for the Technical Publications pipeline. This is not a prompt-writing role. It is a systems-engineering role for someone who has already delivered production AI systems, understands evaluation and rollback, and knows how to make probabilistic systems useful in enterprise environments with structured content, strict review requirements, security boundaries, and real operational consequences. The right candidate can expand system capability while managing token usage, context efficiency, and cost at scale.</p> <h3>What You’ll Do</h3> <ul> <li>Build production-grade LLM applications for documentation and knowledge-work workflows.</li> <li>Design orchestration for multi-step workflows that coordinate models, tools, and deterministic services safely.</li> <li>Optimize context management, token usage, caching, and workflow design for cost, latency, and task success.</li> <li>Build retrieval, structured-output, and tool-integrated systems that safely interact with enterprise content, source control, issue tracking, and documentation systems.</li> <li>Design evaluation frameworks and regression testing for prompts, models, tools, retrieval pipelines, and end-to-end workflows.</li> <li>Establish observability, auditability, rollback, and safe failure modes