Senior Machine Learning Engineer, Query Intelligence

About the Role

This organization operates in the online accommodation and experience marketplace industry. It addresses the challenge of connecting guests with hosts worldwide by offering unique stays and experiences that enable authentic community engagement. The platform supports over 5 million hosts and has facilitated more than 2 billion guest arrivals globally.

The Senior Machine Learning Engineer, Query Intelligence will focus on improving the initial stage of search by enhancing query understanding. This role is critical in accurately interpreting user intent through technologies such as autocomplete, query tagging, expansion, and intent modeling, across various service categories. The position contributes to improving search relevance and recommendation quality, directly impacting the guest experience and marketplace health.

Responsibilities

• Develop and enhance machine learning models for query understanding using large-scale structured and unstructured data.
• Create and improve capabilities including autocomplete, smart compose, query tagging, query expansion, and intent modeling.
• Collaborate with cross-functional teams to identify impactful opportunities, refine requirements, and drive engineering decisions.
• Produce and maintain machine learning models and pipelines for batch and real-time use cases.
• Utilize machine learning tools and infrastructure to build efficient, reusable, and high-performing systems.
• Work on projects involving language generation for search, sequence tagging, query expansion, and user-intent modeling.

Requirements

• Minimum 5 years of industry experience in applied machine learning, with an advanced degree (MS or PhD) in relevant fields.
• Proficiency in programming languages such as Scala, Python, Java, or C++ and strong data engineering skills.
• Deep knowledge of machine learning practices, algorithms, and domains including NLP, personalization, search, and marketplace optimization.
• Experience with technologies like TensorFlow, PyTorch, Kubernetes, Spark, Airflow, Kafka, and data warehouses.
• Proven ability to build end-to-end machine learning models.
• Experience applying large language models and modern NLP techniques including sequence tagging, text generation, and intent classification.
• Familiarity with natural-language, AI-native search experiences is advantageous.
• Understanding of architectural patterns in large-scale software applications.