Booking
Hospitality · Travel booking
Booking currently has 14 active AI-related job listings. The majority of these roles, 50%, are focused on agents, with application and data roles each representing 14%. Engineering is the dominant function, with 12 roles posted. The company is hiring for roles that frequently mention model serving, agent orchestration, and inference infrastructure. In the last 30 days, Booking has posted 11 new AI roles.
Hospitality · Travel booking
Currently tracking 14 active AI roles, with 215 new openings in the last 4 weeks. Primary focus: Agent · Engineering.
Booking currently has 13 active AI-related roles in our index. The most common open titles are: Data Engineer II - GenAI, Data Scientist- Performance Marketing, Director of Technology - Customer Service, Machine Learning Engineer II - Benefits & Pricing Track, Machine Learning Manager - Recommendations & Smart Positioning. Most positions are in Engineering and Product.
Booking's active AI hiring is concentrated in: application (31%), agents (31%), serving infrastructure (15%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Booking is hiring AI talent in: Netherlands (6 roles), Israel (4 roles), United Kingdom (3 roles).
Job postings at Booking most frequently reference: model serving, recommender systems, agent orchestration, llm observability, search ranking.
In the past 30 days, Booking has posted 11 new AI-related roles. That is a +120% change versus the prior 30 days (5 → 11).
| Title | Stage | AI score |
|---|---|---|
| Machine Learning Engineer II - Benefits & Pricing Track Machine Learning Engineer II on the Benefits and Pricing Optimization team within Marketplace Data & AI at Booking.com. The role focuses on building and enabling ML decisioning for price, discounts, and promotions, impacting the customer journey and business. Responsibilities include model evaluation and optimization, data pre-processing, building and deploying scalable ML models for personalized recommendations, and integrating models into production systems. Requires 5+ years of experience, Python proficiency, experience with cloud ML frameworks (AWS SageMaker), ML libraries (TensorFlow, PyTorch, scikit-learn), big data tools (Spark, Airflow, Snowflake), and causal inference/A/B testing. | ServePost-train | 7 |