Software Engineer, Agent Platform

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. ABOUT THE TEAM Anduril’s Lattice software platform integrates together many sensors into a single cohesive view of the world, providing needed context for our users. Anduril’s Frontier AI team builds edge-compatible, generative AI systems into the Lattice software platform to provide features and products that improve autonomy and reduce cognitive burden on the warfighter. Specific applications include but are not limited to automating mission planning, battle-space understanding, voice-control of assets, and enabling higher-levels of autonomy. ABOUT THE JOB Frontier AI is looking for a backend software engineer to own and evolve our internal LLM agent framework. This role sits at the intersection of backend infrastructure, applied AI, agent architecture, model post-training, and evaluation tooling. You will build the platform that enables teams across Anduril to develop, evaluate, and deploy reliable LLM agents in mission-critical environments. WHAT YOU’LL DO Own Anduril’s internal LLM agent framework, including core abstractions, runtime architecture, developer experience, integrations, and reliability. Support multiple business lines building LLM agents by providing new framework capabilities, implementation guidance, architectural reviews, and best-practice patterns. Partner with machine learning teams to make model post-training workflows easy to integrate, ranging from supervised fine-tuning to offline RL, online RL, and environment-driven agent improvement. Design tooling that supports modern agent patterns, including structured tool calling, filesystem-using agents, memory and retrieval, planning loops, subagents, agent graphs, and human-in-the-loop workflows. Work with partner teams to define comprehensive evaluation suites that measure task success, tool-call correctness, trajectory quality, robustness, regressions, and deployment readiness. Stay current on emerging agent architecture and evaluation trends, and make pragmatic decisions about which techniques should or should not be adopted internally. REQUIRED QUALIFICATIONS Strong backend engineering experience building production-quality platforms, frameworks, APIs, or infrastructure used by other engineers. Deep expertise in LLM agent framework design, including the tradeoffs between different orchestration patterns such as linear agents, graph-based agents, multi-agent systems, planner/executor loops, and tool-heavy agents. Experience designing agent evaluation paradigms, including trajectory evaluations, LLM-as-judge workflows, task-success metrics, tool-call correctness checks, rubric-based qualitative grading, adversarial scenario testing, regression eval suites, and human-in-the-loop review. Familiarity with model post-training workflows such as SFT, preference tuning, reinforcement learning, and environment-based agent training. Strong judgment around reliability, observability, debugging, and safety for LLM applications deployed in high-stakes settings. Ability to work directly with partner teams, understand ambiguous product needs, and turn them into reusable platform capabilities. PREFERRED QUALIFICATIONS Experience with agent frameworks like Langchain Deepagents, Claude SDK, etc. Experience building evaluation platforms, simulation environments