Machine Learning Engineer, Agentic AI (Core ML, Community Support)

Role Overview

Machine Learning and Artificial Intelligence are central to the Airbnb product, enhancing everything from Trust to Payments, and Customer Service to Marketing. The Core ML team in Community Support is dedicated to adopting Agentic AI technologies to create an intelligent, scalable, and exceptional customer service experience. This includes developing Chat AI and Voice AI Assistants, and constantly exploring state-of-the-art Agentic architectures. The team develops and enhances various AI models and ML services, leveraging tools such as SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrails for a wide range of applications at Airbnb.

The Difference You Will Make

You will play a crucial 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 undertake are in their early conceptual stages, offering you 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 with 3+ years of experience 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, or AutoGen).
• Experience of shipping production-grade ML/AI systems at scale, with a 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.