ABOUT US

At Booking.com, data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you encounter. The journeys you take. The sights you see. And the memories you make. Through our products, partners and people, we make it easier for everyone to experience the world.

QUALIFICATIONS & SKILLS:

• Masters, PhD, or equivalent experience in a quantitative field (Computer Science, Mathematics, Engineering, Artificial Intelligence, etc.).
• Minimum of 3 years of experience as a Machine Learning Engineer or a similar role, preferably with products that impact a large customer base.
• Strong working experience in one or more general purpose programming languages, including but not limited to: Java, Python or Perl.
• Experience with big data processing frameworks such as , Pyspark, Apache Flink, Snowflake or similar frameworks.
• Experience with cloud frameworks like AWS sagemaker for training, evaluation and serving models using TensorFlow, PyTorch, or scikit-learn.
• Deep understanding of machine learning algorithms, statistical models, and data structures.
• Excellent communication and collaboration skills to work effectively with diverse crafts such as engineering, UX, and product management.
• (Strong plus) Experience in developing large-scale customer-facing search and recommendation systems in an e-commerce environment, including building and maintaining complex multi-stage recommender architecture in high-throughput and low-latency environments.
• (Nice to have) Experience with experimental design, A/B testing, and evaluation metrics for ML models.

ROLE DESCRIPTION:

This role is based in Amsterdam. Booking.com’s Marketplace Department turns travel goals into travel realities. Our purpose? To create frictionless experiences at each and every stage of the trip. From browsing to booking and beyond, our focus is on building the connected experiences which make journeys smoother, faster and more enjoyable. Think of us as travel matchmakers, ensuring seamless integration across our product verticals to effortlessly and intuitively connect traveler preferences and actions.
The Recommendation Applications ML team is dedicated to enhancing the overall customer experience and user journey. The team’s mission is to be the centralized hub for all machine learning (ML) recommendation initiatives, providing scalable, reusable, state-of-the-art ML models. These models aim to increase relevance and personalization, enabling customers to be context-aware and make quicker, more informed decisions. Our team supports the vision of making travelers feel understood and assisted in every step of the travel booking journey.
In 2025, we are planning to focus on expanding and optimizing our recommendation systems, for example by improving personalization and leveraging GenAI to build fine-tuned models for more relevant recommendations.
As a Machine Learning Engineer II, you will be responsible for developing production-grade Machine Learning systems, from models to features and pipelines. You will own the design and delivery of ML systems, from initial idea generation and collecting business requirements from product stakeholders to implementation. You will work closely together with the other Machine Learning Scientists and Machine Learning Engineers in the Intent Discovery track as well as with Data Engineers, Software Engineers and Product Managers.

KEY JOB RESPONSIBILITIES AND DUTIES:

• 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 and refactor and simplify code when necessary.
• Take full ownership of services end-to-end by actively monitoring system health, performance, and business impact. Ensure the relevant service level objectives (SLOs) are met.
• Combine innovation with reuse by abstracting ML/AI concepts from one application to a generalized framework, including leveraging published research, open-source technologies, and previously-developed products at Booking.
• Be responsible for business-related data governance processes, technical implementation, and maintenance of data processing services and storage systems, as well as ML governance processes.
• Evaluate possible architecture solutions considering business and technology requirements.
• Contribute to the internal ML/AI community by sharing knowledge and participating in internal ML programs.
• Continuously evolve your craft by keeping up to date with the latest developments in ML/AI and related technologies and upskilling as needed.