Software Engineer, Agent Platform

About the Role

Anduril's Frontier AI team builds edge-compatible, generative AI systems into the Lattice software platform. This backend software engineer role will own and evolve the internal LLM agent framework. This position sits at the intersection of backend infrastructure, applied AI, agent architecture, model post-training, and evaluation tooling.

Key Responsibilities

• 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 integrate model post-training workflows (e.g., supervised fine-tuning, offline RL, online RL, 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 adoption decisions.

Requirements & Tech Stack

• Strong backend engineering experience building production-quality platforms, frameworks, APIs, or infrastructure.
• Deep expertise in LLM agent framework design, including tradeoffs between 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 in high-stakes settings.
• Ability to work directly with partner teams, understand ambiguous product needs, and translate them into reusable platform capabilities.

Preferred Qualifications

• Experience with agent frameworks like Langchain, Deepagents, Claude SDK.
• Experience building evaluation platforms, simulation environments, benchmark suites, or agent test harnesses.
• Experience with Kubernetes, Docker, distributed systems, workflow orchestration, or ML infrastructure.
• Familiarity with defense, robotics, command-and-control systems, autonomy, or operational planning domains.

US Salary Range

$220,000—$292,000 USD

Benefits

• Highly competitive equity grants.
• Top-tier benefits for full-time employees, including a comprehensive, competitive benefits package at little to no cost to employees.