Senior Staff Machine Learning Engineer, Community Support Engineering

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

The Community You Will Join:

Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb.

The Core ML team is the core team responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb.

The richness of Airbnb's data, the complexity of its marketplace and the variety innate in our product mean that we need to operate at the state of the art of AI practice. We are committed to investing in long term innovation to solve the complex problems we face, and to do that we need the very best experts in ML and AI to join us.

The Difference You Will Make:

As a machine learning engineer or scientist, your expertise will be pivotal in developing AI-powered products to shape the future of the Airbnb customer experience with cutting-edge AI techniques. You will drive and guide the applied science team to apply agile practice for data science, and partner with product managers, software engineers, and operation teams to brainstorm, design and develop AI products and features from inception to production.

A Typical Day:

• Work with large scale structured and unstructured data; explore, experiment, build and continuously improve Machine Learning models and pipelines for Airbnb product, business and operational use cases.
• Work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritize requirements for machine learning, and drive engineering decisions.
• Hands-on develop, product ionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.

Your Expertise:

• Educational Background: PhD in Computer Science, Mathematics, Statistics, or related technical field.
• Industry Experience: 10+ years of experience in building, testing and shipping AI models and products from inception to production; including 2+ years of experience with GenAI.
• Leadership Experience: 5+ years experience leading and guiding applied science/ machine learning teams that deliver large impact as a senior IC.
• Technical Proficiency: Deep knowledge and hands-on experience with Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. NLP, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
• Customer Support Systems: Experience with AI technologies in customer support applications.
• Agile Practice for AI Production: Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.
• Infrastructure Acumen: Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures
• Continuous Learner: A…