Senior Machine Learning Engineer (Relevance and Personalization (Query Intelligence))

Join Airbnb's Relevance and Personalization team as a Senior Machine Learning Engineer. In this role, you will focus on query intelligence, working on critical projects that enhance search and recommendation across the entire Airbnb platform. You will build cutting-edge AI technologies, develop query understanding capabilities, and collaborate with cross-functional partners. The position offers a range of benefits, including paid volunteer time, health food and snacks, generous parental and family leave, learning and development opportunities, and an annual travel and experiences credit.

• Concevoir et développer des modèles de machine learning pour améliorer la compréhension des requêtes, y compris l'autocomplétion, la composition intelligente, le marquage des requêtes et l'expansion des requêtes.
• Collaborer avec des partenaires interfonctionnels pour identifier les opportunités d'impact commercial, affiner et prioriser les exigences pour les modèles de machine learning.
• Développer, produire et exploiter des modèles et des pipelines de machine learning à grande échelle, y compris les cas d'utilisation par lots et en temps réel.
• Familiarity with building natural-language, AI-native and agentic search experiences is a plus
• Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
• Industry experience building end-to-end Machine Learning models
• Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills
• Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning
• 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive)
• Deep understanding of 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. natural language processing, personalization, search and recommendation, marketplace optimization)