Rubrik
Staff Software Engineer - Reliability (US Citizen OR Green Card Only)
$218K–$328K
Palo Alto, CA +1 · Staff · Full-time · Posted May 29, 2026
- Location
- Palo Alto, CA
Palo Alto, CA, United States - Experience
- 8–12+ years in software engineering and production cloud infrastructure; at least 5+ years in formal SRE, DevOps, or Platform engineering.
- Education
- BS, MS, or PhD in Computer Science, Computer Engineering, or a highly related technical discipline.
- Visa sponsorship
- Must be a US Citizen currently residing on CONUS soil.
About the job
Leads reliability architecture and operations for Rubrik’s distributed cloud platform while directing the Application-SRE team and advancing AI infrastructure and AI-enabled SRE practices.
What you'll do
- Set cloud infrastructure architecture and reliability standards; build automation and cost/capacity tooling.
- Lead incident response, SLO governance, and cross-functional reliability work, including customer escalations and POCs.
- Direct the Application-SRE team and deploy reliable, secure AI infrastructure and AI-driven operational capabilities.
What they ask for
- Must be a US Citizen currently residing on CONUS soil.
- BS, MS, or PhD in Computer Science, Computer Engineering, or a highly related technical discipline.
- 8–12+ years of software engineering and production cloud infrastructure experience, including at least 5+ years in formal SRE, DevOps, or Platform engineering for hyperscale SaaS products.
- Hands-on expertise in Golang, Python, or Java; experience designing and operating large-scale distributed systems and Unix/Linux environments.
Nice to have
- Production experience with Kubernetes (GKE, EKS), MySQL, and large-scale databases.
- Experience with compliance frameworks, Infrastructure-as-Code, observability tools, or AI/LLM-powered internal tooling.
Pay
$218,300 — $327,500 USD (base salary)
Tools
Kubernetes, MySQL, Go, Python, Java, GKE, EKS, Terraform
AI focus
Operate reliable, secure, cost-aware AI infrastructure for SaaS and apply agentic and LLM-based approaches to triage, incident response, operational analysis, and engineering productivity; establish guardrails for AI adoption.
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