Anduril
Staff Site Reliability Engineer
Costa Mesa, California, United States · Staff · Full-time · Posted Oct 27, 2026
- Experience
- 10+ years; site reliability engineering, production engineering, infrastructure engineering, or closely related discipline, including architecture or platform-wide scope.
- Education
- Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience.
- Visa sponsorship
- U.S. Person status is required because the position needs access to export-controlled data.
About the job
Sets reliability architecture and standards for CorpTech Platform’s business and manufacturing systems, enabling safe deployments, effective incident management, and scalable operations.
What you'll do
- Design and operate observability, deployment, and incident-management infrastructure; define SLOs and production-readiness standards.
- Lead response to complex incidents and drive systemic reliability, capacity, performance, and cost improvements.
- Provide technical direction and influence reliability decisions across engineering teams, including patterns for AI-enabled systems.
What they ask for
- 10+ years in site reliability, production, infrastructure engineering, or a related discipline, including architecture or platform-wide scope.
- Experience owning reliability infrastructure used by multiple teams and defining SRE standards adopted across engineering.
- Fluency in distributed systems, Kubernetes, cloud platforms (AWS, GCP, or Azure), networking, storage, and their failure modes; proficiency in Go, Python, Rust, or equivalent.
- Degree in a related technical field or equivalent practical experience; U.S. Person status required.
Nice to have
- Experience with Datadog, Grafana, Prometheus, or OpenTelemetry at organizational scale.
- Experience with release-safety systems, AI/ML reliability, AI development tools, high-growth environments, or enterprise systems.
Tools
Kubernetes, AWS, GCP, Azure, Go, Python, Rust, OpenTelemetry
AI focus
Develop reliability patterns for AI-enabled systems, including monitoring model behavior drift and non-deterministic output degradation, graceful fallback, and reliability engineering for AI/ML-serving systems.
Model-written summary · the original posting is authoritative