ML Engineer II - Remote

## About the Company Airbnb is a global platform connecting millions of hosts and guests through unique stays and experiences around the world. Since its founding in 2007, Airbnb has grown to more than 5 million hosts and over 2 billion guest arrivals across nearly every country. Machine learning and artificial intelligence are central to Airbnb’s products and services, supporting areas ranging from Trust and Payments to Customer Service and Marketing. ## About the Role Airbnb is seeking a Senior Machine Learning Engineer to join the Core ML team within Community Support Engineering. The team focuses on applying advanced Agentic AI technologies to create intelligent, scalable, and exceptional customer service experiences. You will contribute to AI-powered customer support products, including Chat AI and Voice AI assistants, while exploring state-of-the-art agentic architectures and developing advanced ML models and services. This role provides an opportunity to take innovative AI concepts from early-stage ideas through production and help shape the future of personalized, proactive customer support for Airbnb guests and hosts. ### Key Responsibilities * Develop novel machine learning systems, product integrations, and performance optimizations to solve real-world customer support challenges. * Design and build efficient AI solutions for Airbnb Customer Service products in collaboration with Product, Design, and Engineering teams. * Develop and enhance AI models, ML services, and agentic architectures. * Explore and apply technologies including supervised fine-tuning, reinforcement learning, distillation, RAG/search, LLM evaluation, testing automation, feedback-based learning, and AI guardrails. * Contribute to the development of Chat AI assistants, Voice AI assistants, and other intelligent customer service solutions. * Evaluate emerging AI and ML technologies and share knowledge and best practices with the broader team. * Help transform early-stage concepts into reliable, production-ready AI solutions. ### Required Qualifications * PhD or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field with 6+ years of experience, or equivalent industry experience. * Hands-on experience with large language models, including pretraining, fine-tuning, SFT, RLHF, GRPO, prompt engineering, RAG architectures, and LLM evaluation frameworks. * Experience building Agentic AI systems involving multi-agent orchestration, tool use, planning, memory, and autonomous reasoning pipelines. * Familiarity with agentic frameworks and approaches such as ReAct, LangGraph, AutoGen, or similar technologies. * Experience shipping production-grade ML and AI systems at scale. * Strong understanding of ML infrastructure, model serving, and MLOps best practices. * Excellent communication and collaboration skills across Engineering, Product, and Design organizations. ### Work Arrangement This is a US Remote Eligible position. The role may occasionally require work at an Airbnb office or participation in company offsites, as agreed upon with the manager. Employees must reside in a state where Airbnb, Inc. has a registered entity. The list of eligible and excluded states may change over time. If the position is employed by another Airbnb entity, the recruiter will provide applicable location eligibility details. ## Equal Opportunity Airbnb is committed to building an inclusive workplace and attracting talent from a broad range of backgrounds. The company believes diverse perspectives and ideas support innovation, engagement, and the development of better products and services. Airbnb encourages qualified individuals to apply and strives to provide an accessible, disability-inclusive application and interview process. Reasonable accommodations are available for candidates who require them. Airbnb supports equality and fair employment opportunities regardless of sex, gender, race, color, religion, national origin, age, disability, or other characteristics protected by applicable law. Show more Show less