ScaleOps

Kubernetes optimization platform for cost reduction

AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeRemote in USANo salary listed

About the Job

Requirements

Have 2 or more years of experience as a software or AI engineer.
Have hands-on experience with Claude and ChatGPT and be able to use them to produce results and create process automation.
Be comfortable working with application programming interfaces and integrations, with experience in customer relationship management and sales tools.
Be able to communicate with sales representatives, understand their challenges, and explain trade-offs clearly.
Have a product mindset and care about adoption as well as functionality.
Be a native Hebrew speaker.

Responsibilities

Work as an AI engineer within the company, serving the go-to-market organization, including Sales, Sales Engineering, Customer Success, and Marketing; identify time-consuming workflows and deliver AI agents and automations that are adopted.
Work US hours alongside the field team and act as a bridge between go-to-market and the AI platform team.
Work with Sales Engineers, Technical Account Managers, Account Managers, and Account Executives to map workflows such as account research, call preparation, deal reviews, renewals, follow-ups, and reporting.
Build and ship agents and automations on the internal platform and connectors, including Salesforce, Gong, Slack, and Google Workspace.
Turn requests into scoped solutions, iterate with users weekly, and measure adoption and time saved.
Write and maintain skills, prompts, and evaluations to maintain quality as models and data change.
Share learnings with the platform team, including missing integrations, reusable components, and common failure modes.
Train go-to-market users to use AI tools more effectively and spread effective working patterns across the team.

Desired Qualifications

Python hands-on experience building applications and agents based on large language models, including tool use, Model Context Protocol, retrieval-augmented generation, and evaluations.
Experience at a business-to-business software-as-a-service or infrastructure/DevOps company.
Customer-facing, solutions, or forward-deployed engineering experience.

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

This is an AI engineer role inside the company. Your “customer” is the GTM organization (Sales, SEs, Customer Success, Marketing), and your job is to embed with them, find the workflows that eat their time, and ship AI agents and automations that actually get used. You’ll work US hours alongside the field team and be the bridge between GTM and the AI platform team.

Sit with SEs, TAMs, AMs and AEs to map their real workflows: account research, call prep, deal reviews, renewals, follow-ups, reporting.
Build and ship agents and automations on top of our internal platform and connectors (Salesforce, Gong, Slack, Google Workspace and more).
Turn rough requests into scoped solutions, iterate with users weekly and measure adoption and time saved.
Write and maintain skills, prompts and evals so quality stays high as models and data change.
Feed what you learn back to the platform team: missing integrations, reusable components, common failure modes.
Train GTM users to get more out of AI tools and spread working patterns across the team.

What You'll Bring

2+ years as a software or AI engineer, ideally with customer-facing, solutions or FDE experience.
Deep hands-on experience with Claude and ChatGPT: you know how to get real results out of them and how to turn that into process automation.
Comfortable with APIs and integrations; experience with CRM and sales tooling.
Excellent communication: you can sit with a sales rep, understand the pain and explain trade-offs clearly.
Product mindset: you care whether something gets adopted, not only whether it runs.
Native Hebrew speaker.

Nice to have

Python hands-on experience building LLM-based applications and agents (tool use, MCP, RAG, evals).
Experience at a B2B SaaS or infrastructure/DevOps company.

About the Company

ScaleOps

Kubernetes optimization platform for cost reduction

ScaleOps provides a cloud cost-optimization platform for Kubernetes environments as a SaaS. It automatically manages container resources by rightsizing CPU and memory in real time based on application demand, eliminating manual tuning. It also offers Smart Pod Placement to consolidate pods across nodes when possible, reducing underutilized capacity and lowering infrastructure costs. This combination helps clients improve workload capacity and performance while cutting cloud spend, with case studies reporting up to 50% savings. The platform differentiates itself by end-to-end automated resource management and intelligent placement within Kubernetes, targeting enterprises that run cloud-native applications. ScaleOps’ goal is to help organizations run Kubernetes more efficiently and cost-effectively through subscription-based access to its optimization engine.

Company size: 51-200
Company stage: Series C
Total funding: $209.5M
Headquarters: Tel Aviv-Yafo, Israel
Founded: 2022

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