Machine Learning Engineer, Agentic AI

About the Role

Machine Learning and Artificial Intelligence are at the heart of the Airbnb product, impacting areas from Trust to Payments, and Customer Service to Marketing to enhance guest and host experiences. The Core ML team within Community Support is pioneering the adoption of Agentic AI technologies to deliver intelligent, scalable, and exceptional customer service. This includes developing Chat AI and Voice AI assistants. The team continuously explores state-of-the-art Agentic architectures, develops and enhances AI models and ML services, and utilizes tools such as SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrail for various applications across Airbnb.

The Difference You Will Make

You will play a crucial role in leveraging cutting-edge AI technologies to revolutionize Airbnb’s customer service, aiming to provide personalized, user-friendly, and proactive experiences. Many of the initiatives you will work on are in their early conceptual stages, offering you the unique opportunity to shape these ideas from their inception through to production, transforming visionary concepts into tangible, impactful realities.

A Typical Day

• Champion the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.
• Work cross-functionally with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb CS products.
• Learn and share the latest AI/ML technologies with the team.

Your Expertise

• PhD or Master's degree w/ 3+ YOE in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field — or equivalent industry experience.
• Hands-on expertise in LLM, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG architectures, and LLM evaluation frameworks.
• Experience building Agentic AI systems — including multi-agent orchestration, tool-use, planning, memory, and autonomous reasoning pipelines (e.g., ReAct, LangGraph, AutoGen, or similar).
• Experience of shipping production-grade ML/AI systems at scale, with deep understanding of ML infrastructure, model serving, and MLOps best practices.
• Excellent communication skills with the ability to collaborate effectively across Engineering, Product, and Design organizations.