Site Reliability Engineer

Runpod is a foundational platform for developers to build and run custom AI systems that scale. The Site Reliability Engineer will focus on ensuring the stability and resilience of Runpod’s distributed platform by partnering with engineering teams to improve system design, strengthen observability, and prevent incidents before they happen.

Responsibilities:

• Define and implement SLIs/SLOs for critical services
• Lead incident response and coordinate cross-team mitigation efforts
• Conduct blameless postmortems and ensure corrective actions are completed
• Perform production readiness reviews for new services and features
• Identify systemic risks and drive preventative improvements
• Design and improve monitoring, alerting, and dashboards (Prometheus, Grafana, etc.)
• Improve signal-to-noise ratio in alerts and reduce alert fatigue
• Build internal tooling for reliability tracking and reporting
• Improve visibility into GPU performance and distributed systems health
• Automate recurring operational workflows
• Build tools and scripts (Python, Go, Bash) to eliminate manual processes
• Improve deployment safety through automation and guardrails
• Strengthen CI/CD reliability and release processes
• Partner with engineering teams to improve system resilience
• Provide guidance on fault tolerance, scalability, and failure handling
• Contribute to architectural discussions with a reliability-first mindset

Requirements:

• 5+ years of experience in SRE, Reliability Engineering, or Production Engineering
• Strong Linux systems and Networking expertise
• Experience managing containerized production systems
• Strong understanding of distributed systems and failure modes
• Experience defining and managing SLIs/SLOs
• Proven incident response and postmortem leadership experience
• Strong scripting or programming skills
• Experience with monitoring and alerting systems
• Excellent written communication skills
• Successful completion of a background check
• Experience with GPU infrastructure or AI/ML platforms
• Experience improving reliability in high-growth or large scale environments
• Familiarity with GPU observability tooling
• Experience with Infrastructure as Code
• Experience working in startup environments
• Experience building internal reliability platforms or frameworks