AI Platform Engineer

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%.

As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments.

Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute.

What You'll Be Doing

Build and run the internal platform ScaleOps R&D builds on: CI/CD, Kubernetes tooling, developer experience, and infrastructure automation

Give every engineer a fast, safe path to develop, test, and ship code to production

Build and connect AI tools for R&D and the company, and bring new tools and best practices from the market into how we build the product

Build infrastructure for the other R&D teams, from internal agentic platforms to the foundations that LLM-powered automation runs on

Partner closely with backend, product, and R&D teams, and treat engineers as your customers: measure adoption and time saved

Identify and eliminate friction in the development lifecycle through automation and self-service tooling

What You'll Bring

5+ years of hands-on experience in platform engineering, infrastructure, or AI/ML infrastructure in high-scale environments

Experience building self-service platforms or tooling that other engineers adopted

Strong hands-on experience with Kubernetes, cloud (AWS, GCP, or Azure), and infrastructure-as-code

Strong development skills: you have written production code, ideally Go, such as Kubernetes operators or controllers

A builder mindset, strong ownership, and curiosity about the AI market and its tooling

Full professional fluency in Hebrew and English (ב-US: “Full professional fluency in English”)

(Nice to have) Open-source contributions in the cloud-native space (Karpenter, Cilium, KEDA, Argo, Kueue)

(Nice to have) Experience with LLM serving, agentic frameworks, or MCP-based tooling

(Nice to have) Experience with scheduling, node lifecycle, or resource management in Kubernetes