Who are we?
Komodor is a cutting-edge Kubernetes platform built by developers, for developers. We help engineering and infrastructure teams manage complex systems with ease, efficiency, and transparency — so they can focus more on innovation and less on firefighting Kubernetes challenges.
Our platform is trusted by thousands of teams worldwide, with standout capabilities like Klaudia – our AI-powered Kubernetes failure detection and analysis engine that delivers real-time insights to dev and infra teams; the Cost team, helping companies dramatically reduce cloud spend; the Health team, building industry-leading troubleshooting features; and our Operations group, crafting powerful Kubernetes-native agents, operators, and controllers.
Your mission
Build the future of Kubernetes troubleshooting. You'll design innovative backend solutions powered by AI and help shape where we're headed as a company.
Why This Role Rocks?
Shape our tech direction - You'll have a real voice in our technical strategy!
What You'll Bring:
What We Offer:
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Komodor enables enterprises to unlock the full potential of Kubernetes at scale. Our pioneering Kubernetes Management Platform eliminates complexity across the entire Kubernetes stack, to drive efficiency, empower developers, and optimize cost and performance.
Komodor is the leading Autonomous AI SRE Platform for cloud native infrastructure and operations. Powered by Klaudia Agentic AI, Komodor automatically visualizes, troubleshoots, and optimizes Kubernetes-based platforms at scale.
Trusted by companies like Dell, Cisco, BlackRock, Balyasny, Rockwell Automation, Priceline, and OpenTable, Komodor is the go-to platform for innovative enterprises who need a comprehensive, autonomous solution for managing and optimizing cloud-native infrastructure and operations at scale.