Machine Learning Engineer, Agentic AI, Community Support

Role Overview

Machine Learning and Artificial Intelligence are at the heart of the Airbnb product, impacting areas from Trust to Payments, Customer Service, and Marketing to enhance guest and host experiences. The Core ML team in Community Support is pioneering the adoption of Agentic AI technologies to deliver an intelligent, scalable, and exceptional customer service experience. This includes developing Chat AI assistant, Voice AI Assistant, and more. The team actively explores State-of-the-Art Agentic architecture, develops and enhances various AI models and ML services, leveraging tools like SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrails for diverse Airbnb applications.

The Difference You Will Make

You will play a pivotal role in transforming Airbnb’s customer service by building and leveraging cutting-edge AI technologies to deliver personalized, easy-to-use, and proactive customer service experiences. Many initiatives you will work on are in their early conceptual stages, offering you the unique opportunity to shape these ideas from inception to production, turning visionary concepts into 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.