Drimva

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.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 5, 2026 (UTC)