Site Reliability Engineer

Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on.   We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.   Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers. The Reliability team owns the availability, performance, and operational excellence of Runpod’s global platform. While infrastructure teams build the systems, the Reliability team ensures those systems remain resilient, observable, and scalable under real-world production conditions. This team is responsible for: - Defining and enforcing reliability standards across engineering - Designing incident response processes and improving recovery times - Building observability systems and reliability tooling - Driving SLO adoption and production readiness reviews - Reducing operational toil through automation The Reliability team works cross-functionally with Infrastructure, Product Engineering, and Support to ensure our systems remain stable and performant as we scale rapidly. We value proactive problem solving, automation-first thinking, and strong ownership of production systems. As a Site Reliability Engineer on the Reliability team, you will focus on ensuring the stability and resilience of Runpod’s distributed platform. You will partner with engineering teams to improve system design, strengthen observability, and prevent incidents before they happen. This role blends software engineering with production operations. You’ll work on reliability frameworks, SLO design, automation, and production hardening, reducing errors and improving performance across different services and infrastructure. This is a high-impact role central to maintaining trust with developers running critical AI workloads on Runpod. Your Impact - Increase platform uptime and reduce incident frequency and duration - Establish and operationalize SLIs/SLOs across services - Improve MTTR through better tooling, automation, and runbooks - Strengthen production readiness standards - Drive long-term systemic reliability improvements You will influence how reliability is defined and measured across Runpod and help build the operational backbone of the company. Responsibilities: Reliability Engineering - 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 Observability & Monitoring - 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 Automation & Toil Reduction - 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 Cross-Functional Reliability Advocacy - 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 Preferred: - 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 What You’ll Receive: - The competit