Machine Learning Engineer - Traveler Intelligence

Role Description

As a Machine Learning Engineer (MLE I), you contribute significantly to ML projects, taking ownership of tasks and driving them to completion. You are expected to work independently and proactively, leveraging your technical expertise to solve complex problems while collaborating closely with your team. You focus on technical execution and continuous learning, contributing to our engineering culture by sharing knowledge and insights.

Understanding our travelers is a top priority h millions of accommodations, flights, taxis, attractions, and car rentals available on our platform, we help people around the world find the best options for their journeys. Achieving this requires a deep understanding of traveler preferences, including those explicitly expressed through searches and filters, as well as those inferred from behavioral signals such as clicks and bookings.

We also need to accurately interpret traveler intent: why are they on our platform right now? Are they exploring options or already close to making a decision? Are they continuing a previous session, possibly on another device? And are they extending an existing trip as part of a Connected Trip, or starting an entirely new one?

The Traveler Intelligence track, part of ’s Marketplace organization, develops the core capabilities that make personalization at scale possible. It brings together traveler data and behavioral signals into a unified, real‑time understanding of each traveler, enabling every product surface and AI‑driven interaction to become more relevant and timely. By applying advanced Data & AI techniques to interpret behavior and uncover intent, the track helps deliver more personalized, seamless, end‑to‑end experiences across the entire Booking platform.

• Develop production‑grade ML systems, from models to features and pipelines, accounting for reliability, scalability, real‑time requirements, monitoring and retraining.
• Build readable and reusable code, applying code quality best practices and using standard libraries. Choose the right technology or coding methodology as well as refactor and simplify code when necessary.
• Take full ownership of your services end to end by actively monitoring the systems health, performance and business impact.
• Be responsible for business related data governance processes, the technical implementation and maintenance of data processing services and storage systems, and the implementation and maintenance of ML governance processes.
• Evaluate possible architecture solutions taking into account the business and technology requirements.
• Set the relevant service level objectives (SLOs) and act accordingly when they are not met.
• Build readable and reusable code, using the right technologies and coding methodologies applying knowledge of business area tools and product needs.
• Continuously evolve your craft by keeping up to date with the latest developments in ML/AI and related technologies and upskilling on these, as needed.
• Contribute to the internal ML/AI community by sharing your knowledge and participating in our internal ML programs.
• Coach others through evidence and clear communication, explaining advanced technical concepts in simpler terms.
• Maintain a highly cross‑disciplinary perspective, solving issues by applying approaches and methods from across a variety of disciplines and related fields.
• Achieve mutually agreeable solutions by staying adaptable, communicating ideas in clear coherent language and practicing active listening.

• 2+ years of relevant work experience (or equivalent), involved with the application of Machine Learning to business problems in a commercial environment.
• Experience in areas like e.g. Recommender Systems, Deep Learning, Information Retrieval, Computer Vision, Speech Recognition, Causal Inference, MLOps, etc.
• Work experience (or equivalent), involved with the application of Machine Learning to business problems, preferably in a commercial environment.
• Knowledge of multiple machine learning facets, such as working with large data sets, experimentation, scalability and optimization.
• Experience with…