[Remote] Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)

Note: The job is a remote job and is open to candidates in USA. Airbnb is a global company focused on creating unique stays and experiences for guests. They are seeking a Senior Machine Learning Engineer to enhance query intelligence for their search and recommendation systems, focusing on natural language processing and machine learning models to improve user interactions.

for machine learning models, drive engineering decisions, and quantify impact • Hands-on develop, productionize, 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 • Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling Skills • 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields • Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills • 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) • Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive) • Industry experience building end-to-end Machine Learning models • Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning • 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)