Machine Learning Engineer, Core ML - Community Support

About the Community

Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing, ML is relied upon to ensure that guests and hosts have the best possible experience with Airbnb. The Core ML team in Community Support is responsible for adopting Agentic AI technologies to enable an intelligent, scalable, and exceptional customer service experience, including developing the Chat AI assistant, Voice AI Assistant, and more. The team constantly explores SOTA Agentic architecture, develops and enhances various AI models, ML services, and leverages tools including SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrail for a wide range of applications in Airbnb.

The Difference You Will Make

We believe current customer experiences in these domains are only scratching the surface of the innovations that are possible, and that science is at the heart of delivering a step-function change for our Guest and Host on Airbnb. You will build and leverage cutting-edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use, and proactive customer service experiences. Many of the initiatives you’ll tackle are in their early conceptual stages. You will have the 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.