AI Operations Engineer, Americas

About the role

ABOUT CODERABBIT

CodeRabbit is the governance and control layer for agentic software development, helping engineering teams trust the code they ship. AI can generate code faster than people can review and understand it. Teams still need to know whether each change solves the right problem, fits their architecture, and is safe to ship.

We provide independent validation grounded in codebase context and team standards, giving people the evidence they need to judge what should ship. Across Review, Triage, Explain, and Secure, our platform challenges changes before merge, prioritizes pull requests by value, impact, and risk, explains what changed and why it matters, and monitors the codebase for security vulnerabilities after merge.

CodeRabbit serves nearly 20,000 customers and supports approximately 300,000 open-source repositories, performing over 2 million code reviews each week. Our $143M Series C, raised at a $1.5B valuation, supports further product development and international expansion.

Locations: San Fransisco, CA or Boston, MA (Boston preferred)- Hybrid

ROLE OVERVIEW

At CodeRabbit, we seek an exceptional Support Operations Engineer who identifies operational gaps, designs solutions autonomously, and executes end-to-end. You will own the full lifecycle, from spotting inefficiencies in how support operates, to architecting the fix, to building and deploying the solution yourself. You'll be hands-on with code, automation, integrations, and data systems. You'll think strategically about frameworks and processes, then immediately build what's needed without waiting for someone else. You work across Product, Engineering, Services, and Support to understand pain points, but you drive the technical solutions yourself. This is a role for someone who sees a problem, owns it completely, and ships.

KEY RESPONSIBILITIES

1. Identify gaps in support operations and autonomously design, build, and deploy automation systems and AI-powered workflows that improve team productivity, customer satisfaction, and support quality

2. Spot inefficiencies in how support operates and build the operational frameworks—automated playbooks, process integrations, quality systems—that fix them at scale

3. Autonomously build dashboards, metrics systems, and data pipelines that expose operational gaps and guide improvements

4. Own the full problem-to-solution cycle: identify recurring issues, architect technical fixes, and execute the automation, integrations, and tooling yourself

5. Build and own the knowledge management system from the ground up—automated runbooks, playbooks, FAQs, documentation—that prevents issues before they happen

6. Engineer and deploy AI-powered workflows and automation end-to-end: identify where they're needed, build them, integrate them, optimize them

7. Evaluate, architect, and implement the support tooling stack—independently making decisions and building integrations that scale operations