Senior Machine Learning Engineer, Relevance and Personalization (query Intelligence)

Airbnb Airbnb · Consumer · United States · Engineering & Technology

Senior Machine Learning Engineer focused on Relevance and Personalization at Airbnb, specifically query intelligence. The role involves building and improving ML models for search and recommendation, with a focus on query understanding using NLP and LLMs. Responsibilities include developing models for autocomplete, intent classification, and natural language search experiences, productionizing ML pipelines, and collaborating with cross-functional teams.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
  5. 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.

Skills

Required

  • 5+ years of industry experience in applied Machine Learning
  • MS or PhD in relevant fields
  • Strong programming (Scala / Python / Java / C++ or equivalent)
  • data engineering skills
  • Deep understanding of Machine Learning best practices
  • algorithms (eg. neural networks/deep learning, optimization)
  • 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.

Nice to have

  • Familiarity with building natural-language, AI-native and agentic search experiences

What the JD emphasized

  • build cutting-edge AI technologies
  • build the models that parse free-form and natural-language multimodal queries
  • Experience applying large language models and modern NLP
  • building natural-language, AI-native and agentic search experiences

Other signals

  • building models for query understanding
  • applying large language models and modern NLP
  • productionize and operate Machine Learning models and pipelines at scale