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CorpTech Platform is the internal engineering force multiplier behind Anduril's corporate systems. It drives CorpTech 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 ArsenalOS, the digital backbone of Anduril's hardware enterprise. By unifying systems, accelerating development velocity, and embedding intelligence into every layer, CorpTech 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, we are rethinking how software is built, tested, deployed, and evolved by integrating AI directly into the engineering lifecycle. CorpTech Platform is how Anduril scales its business systems with the same rigor, speed, and adaptability as its mission critical products.

This Staff SRE role sets the reliability architecture for the systems that run Anduril's business and manufacturing operations. You are not responding to tickets. You are the person who decides how production works: how services are observed, how deployments are safe, how incidents are managed, and how reliability scales as the platform grows. You own the infrastructure and mechanisms that make reliability a property of the platform rather than a function of individual heroics.

The role carries broad organizational reach. You will influence how software engineering teams across CorpTech Platform build and operate their systems, set the standards that govern production readiness, and make infrastructure decisions that affect every service in the portfolio. You bring both deep systems expertise and the judgment to know which reliability investments matter most at each stage of the platform's growth.

• Set the reliability architecture for CorpTech Platform's production environment, including observability infrastructure, deployment systems, incident management, capacity planning, and failure-domain isolation.
• Design and operate the observability platform, including metrics, distributed tracing, structured logging, alerting, and dashboarding infrastructure that gives engineering teams real production visibility at scale.
• Own deployment infrastructure and release-safety mechanisms, including progressive rollout systems, canary analysis, automated rollback, and deployment gates that let the organization ship fast without gambling on production stability.
• Define and govern SLO frameworks that make reliability measurable and actionable, creating shared language between SRE, product engineering, and leadership for making trade-off decisions.
• Identify systemic reliability risks across the platform and drive infrastructure or automation investments that eliminate entire failure classes rather than patching individual symptoms.
• Establish production-readiness standards and review processes that engineering teams adopt during design and pre-launch, embedding reliability into the development lifecycle rather than bolting it on after release.
• Lead incident response for complex, multi-system failures and drive post-incident processes that produce durable systemic improvements rather than shallow fixes.
• Develop reliability patterns for AI-enabled systems, including monitoring for model behavior drift, non-deterministic output degradation, and graceful fallback under novel failure modes.
• Provide technical direction to SRE and infrastructure engineers, facilitate cross-team architecture decisions, and represent reliability concerns in platform-level planning and prioritization.
• Drive capacity planning, cost optimization, and performance engineering for the platform's critical paths and shared infrastructure.

• 10+ years of experience in site reliability engineering, production engineering, infrastructure engineering, or a closely related discipline, including experience operating at architecture or platform-wide scope.
• Demonstrated experience designing and owning reliability infrastructure that multiple engineering teams depend on, including observability platforms, deployment systems, or incident management tooling at meaningful scale.
• Deep technical fluency across distributed systems, container orchestration (Kubernetes), cloud platforms (AWS, GCP, or Azure), networking, storage, and the failure modes specific to each layer.
• Proficiency in systems programming languages (Go, Python, Rust, or equivalent) used for building production infrastructure, tooling, and automation.
• Demonstrated experience defining SRE standards, production-readiness frameworks, or operational maturity models and influencing adoption across engineering teams without formal authority.
• Track record of leading incident response for complex, multi-system failures and converting post-incident findings into infrastructure investments that prevent recurrence.
• Proven ability to work independently through ambiguity, define reliability strategy without top-down direction, and sequence investments against competing organizational demands.
• Excellent written and verbal communication skills, with the ability to influence senior engineers, engineering leadership, and cross-functional partners on infrastructure and reliability decisions.
• 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 designing or operating observability platforms (Datadog, Grafana, Prometheus, OpenTelemetry) at organizational scale, including decisions about instrumentation standards, data pipelines, retention, and cost management.
• Experience building deployment and release-safety infrastructure, including canary systems, progressive delivery, feature flagging, and automated rollback mechanisms.
• Experience with reliability engineering for AI-enabled or ML-serving systems, including monitoring non-deterministic behavior, building evaluation-driven alerts, and designing graceful degradation for model failures.
• Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows.
• Experience in hyper growth startup-like environments, with demonstrated success scaling reliability practices alongside rapid engineering growth.
• Familiarity with enterprise systems and business process domains such as ERP, MES, WMS, CRM, finance systems, or manufacturing systems.
• Eligible to obtain and maintain a U.S. Secret security clearance.