Senior Quality Engineer, Applied AI

About the Team

Corp Tech Platform is the internal engineering force multiplier behind Anduril’s corporate systems. It drives Corp Tech engineering forward through strategic investment in data platforms, software platforms, QA and release excellence, ERP engineering, and AI infrastructure. We build the foundations that power both CorpOS, enabling Finance and Growth to operate with speed and precision, and Arsenal

OS, the digital backbone of Anduril’s hardware enterprise. By unifying systems, accelerating development velocity, and embedding intelligence into every layer, Corp Tech Platform transforms how the company operates end to end. We are also driving Anduril toward becoming an autonomous enterprise through initiatives like the Autonomous Software Factory, rethinking how software is built, tested, deployed, and evolved by integrating AI directly into the engineering lifecycle.

Corp Tech Platform is a strategic engine that scales the company’s business systems with the rigor, speed, and adaptability of its mission critical products.

• Act as the quality and reliability lead within a small, senior AI development pod focused on solving novel, high-impact business and engineering problems.
• Design and implement test strategies, validation approaches, and release readiness criteria for AI-enabled software, automation, and agentic workflows.
• Partner closely with software and AI engineers to identify failure modes early across code, prompts, models, integrations, infrastructure, and user workflows.
• Build confidence in fast-moving solutions through automated testing, observability, instrumentation, environment design, and operational safeguards.
• Help define practical standards for reliability, resiliency, debugging, and production operations in an AI‑first development model.
• Contribute directly in code, infrastructure, and tooling where needed to improve delivery confidence, developer feedback loops, and production stability.
• Support launch readiness, production issue response, root‑cause analysis, and continuous improvement for front‑facing systems delivered by the pod.
• Evangelize strong engineering discipline in areas such as quality assurance, release engineering, infrastructure hygiene, and incident prevention without sacrificing speed.

• 5+ years of experience in software engineering, site reliability engineering, quality engineering, infrastructure engineering, or a closely related role in a fast‑paced environment.
• Demonstrated experience building or operating reliable production software systems, including ownership of testing, observability, deployment confidence, and operational readiness.
• Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or platform infrastructure.
• Experience working with frontier AI tooling, AI coding assistants, LLM‑enabled applications, or agentic systems, including awareness of the unique quality and reliability challenges these systems introduce.
• Ability to move between hands‑on implementation and systems‑level quality strategy, with sound judgment on where rigor is required versus where speed is appropriate.
• Strong debugging, root‑cause analysis, and incident response instincts, with a bias toward preventing classes of failures rather than repeatedly reacting to them.
• Excellent written and verbal communication skills, with the ability to influence senior engineers and cross‑functional stakeholders on quality and reliability trade‑offs.
• Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience.
• U.S. Person status is required as this position needs to access export‑controlled data.

• Experience supporting AI‑first or AI‑accelerated software development teams, including designing quality controls around non‑deterministic system behavior.
• Experience with automated testing strategies spanning unit, integration, end‑to‑end, performance, and reliability testing for complex software products.
• Experience with infrastructure as code, cloud platforms, observability stacks, release engineering, and production operations.
• Ex…