Site Reliability Engineer

Runpod

13d•$150,000 - $200,000•Remote

About The Position

Runpod is the AI Developer Cloud, serving over one million developers who use the platform to experiment, train, fine-tune, deploy, and scale AI. The platform has processed more than 20 billion inference requests. Runpod is at an inflection point for AI infrastructure and is building the platform for the next generation of developers. The company is a small, remote-first team that values ownership, speed, and shipping impactful work. The Reliability team is responsible for the availability, performance, and operational excellence of Runpod’s global platform, ensuring systems are resilient, observable, and scalable. This role blends software engineering with production operations, focusing on reliability frameworks, SLO design, automation, and production hardening to reduce errors and improve performance across services and infrastructure. It's a high-impact role central to maintaining trust with developers running critical AI workloads on Runpod.

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

Nice To Haves

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

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