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.

Role Overview

As a Software Engineer at CodeRabbit, you'll be contributing to the development of advanced applied AI systems that revolutionize code review processes. You'll work at the intersection of intelligent systems and software engineering, creating tools that empower developers to iterate rapidly in complex environments. Your expertise will be crucial in developing solutions that enhance code quality, scalability, and developer productivity.

You’ll help architect and build core backend systems — the infrastructure that powers our AI agent workflows, context-enriched code reviews, repo-level planning tools, and dashboard interfaces. You’ll work alongside AI researchers, infra specialists, frontend engineers, and product thinkers to build reliable, intelligent backend systems that power our developer tools at scale.

Responsibilities

Architect and build scalable backend services, APIs, and distributed systems

Collaborate with ML engineers to integrate models into agent-based systems and AI products involving autonomous or task-driven agents

Guide junior engineers through mentorship and technical reviews

Own architectural decisions in new product features

Ensure reliability, performance, and security across systems

Contribute to an engineering culture of speed, quality, and pragmatic innovation

Qualifications

Education: Degree in Computer Science, Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience

Experience: 5+ years building backend systems and distributed infrastructure

Technical Expertise: Fluency in TypeScript and Node.js, or a similar modern backend stack; experience with micro services, async job queues, and event-driven architectures

Systems Thinking: Familiarity with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), CI/CD pipelines, devops tooling, runtime infrastructure, and production observability

Engineering Craft: Strong understanding of software engineering best practices, including testing, code reviews, and version control workflows

Product Mindset: Thoughtful about trade-offs, user experience, and fast iteration

Team Leadership: Willing to mentor, lead by example, and influence technical direction without being a pure manager

Bonus Points

Experience with code search, code graphs, tree-sitter, or static analysis tools, and an understanding of how to apply them in real-world engineering environments

Experience integrating AI/LLM-based systems into product workflows

Contributions to open-source or community projects

Experience designing or implementing agent-based systems or AI products involving autonomous or task-driven agents

Why Join Our Engineering Culture?

CodeRabbit is building the next generation of AI-native developer tooling — starting with code review. We combine large language models with deep software engineering context to help teams ship faster, catch more bugs, and make better architectural decisions at scale.

We are a high-ownership engineering culture. That means no passive execution, no waiting for perfect tickets, and no narrowly defined task boundaries. Engineers here find problems before they're assigned, use AI as a core part of how they build, ship with judgment, and own outcomes from proposal to production.