Senior Machine Learning Engineer, Relevance and Personalization

Responsibilities

• Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.
• Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language ("search in your own words") search experiences powered by modern NLP and LLMs.
• Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements 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.

Requirements

• 5+ years of industry experience in applied Machine Learning, inclusive of 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 (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (e.g. 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 (e.g. 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.
• Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).

Nice-to-Haves

• Familiarity with building natural-language, AI-native and agentic search experiences is a plus.