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