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CodeRabbitCodeRabbitSan Francisco, CA

Applied AI Engineer

Designs, builds, and deploys generative AI systems including LLM-based code reviews, agentic workflows, and RAG pipelines for developer productivity tools. Requires 3+ years in ML/LLM production systems, Python/TypeScript proficiency, and AI frameworks like LangChain.

175k – 275k/yr
Hybrid3+ YOEML Engineering

About the role

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

  • Degree in Computer Science, Engineering, Artificial Intelligence, or related field, or equivalent practical experience
  • 3+ years applying ML or LLM-based systems in real-world production environments, with at least 2 years of industry experience focused on generative AI
  • 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
  • Strong skills in prompt engineering
  • Ability to extract insight from telemetry, logs, user signals, and structured feedback
  • Comfortable applying research-inspired methods to solve concrete product challenges
  • 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

Compensation

Base pay range: $200k-275k (actual salary based on job-related skills, experience, and location). Strong salary, equity, and benefits package.

Skills

PythonTypeScriptLangChainLlamaindexOpenai ApisPineconeLancedbPrompt EngineeringRLHFRAG

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