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Designs, builds, and deploys generative AI systems for code review, including LLM-based systems, RAG retrieval pipelines, and multi-step agentic workflows. Applies RLHF, ranking, and reward modeling to improve response quality. Stack includes TypeScript, Python, LangChain, LlamaIndex, OpenAI APIs, and Pinecone or Lancedb.
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 an Applied Gen AI Engineer at CodeRabbit, you'll play a central role in designing, building, and deploying advanced generative AI systems that power our code review and developer productivity tools. You’ll be responsible for bringing the latest advancements in generative AI to life — integrating techniques like RAG, RLHF, and multi-step agentic reasoning into high-impact product workflows.
You’ll collaborate with engineers, product managers, and technical leads to iterate on intelligent systems that deliver real-world value, improving how developers write, review, and ship code.
Responsibilities
Qualifications
Bonus Points
Why Join Our Engineering Culture?
Our Values
Compensation: $200K - $300K
