Senior Applied AI Engineer

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

• Design and optimize LLM-based systems for high-quality, context-rich code reviews
• Build and refine agentic workflows that reason across multiple steps and contexts
• Develop and maintain knowledge base and retrieval pipelines (e.g., chunking, embeddings, semantic search)
• Deploy generative AI models and pipelines into production and monitor performance
• Collaborate across teams to ensure that AI outputs align with user needs and product goals
• Analyze human-in-the-loop feedback and usage data to iteratively improve system performance
• Apply RLHF, ranking, and reward modeling techniques to improve response quality over time
• Stay current with the latest generative AI developments and apply them to new use cases

Qualifications

• Education: Degree in Computer Science, Engineering, Artificial Intelligence, or related field, or equivalent practical experience
• Experience: 5+ years applying ML or LLM-based systems in real-world production environments, with at least 2 years of industry experience focused on generative AI
• Technical Skills: Strong programming skills in TypeScript and Python
• AI Frameworks: Experience with tooling such as LangChain, LlamaIndex, OpenAI APIs, or vector databases like Pinecone or Lancedb
• Prompt Engineering: Strong skills in prompt engineering
• Data Fluency: Ability to extract insight from telemetry, logs, user signals, and structured feedback
• Practical Mindset: Comfortable applying research-inspired methods to solve concrete product challenges
• Cross-Functional Collaboration: Experience working across product, engineering, and design to deliver production-grade systems

Bonus Points

• Experience optimizing RAG systems and tuning retrieval performance using custom embeddings or search strategies
• Hands-on experience with RLHF pipelines, reward modeling, or behavioral policy tuning in LLMs
• Experience integrating LLM systems into developer tooling or collaborative workflows
• Track record of contributions to open-source projects or publications in applied AI/ML