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.
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 Scientist I - Performance Marketing Machine Learning Scientist role focused on optimizing large-scale ML models for online bidding in performance marketing. The role involves developing advanced ML and optimization techniques for bidding algorithms, modeling user intent and marketplaces, and optimizing bidding strategies to maximize advertising budget efficiency. It also includes designing and implementing scalable evaluation pipelines, synthetic data generation, and benchmarking for model quality. The position requires a strong understanding of large-scale optimization, auction theory, and applying ML to industrial setups, with a focus on end-to-end research-to-production cycles and A/B testing. | AgentEval Gate | 7 |
| Data Scientist- Performance Marketing Data Scientist role focused on building and optimizing large-scale ML models for online bidding in performance marketing. The role involves end-to-end ownership from ideation to implementation, including data consumption, preparation, statistical analysis, modeling, and experimental design to inform business decisions and optimize auction levers. Collaboration with stakeholders and mentoring junior colleagues are also key aspects. |
| Serve |
| 5 |
| Engineering Manager Engineering Manager for Booking.com's Marketplace teams, responsible for leading a high-performing team that maintains and evolves mission-critical systems. The role focuses on creating an environment for engineers to design, build, and operate reliable, scalable platform components, ensuring technical decision-making, software delivery, operational excellence, and sustainable execution. Two potential teams are Conversational Platform (handling text, voice, video conversations for travelers, processing millions of messages daily, routing to human or AI actors) and Order Platform (source of truth for reservation data, orchestrating creation and changes). Responsibilities include people leadership, technology/craft/delivery guidance, architecture, and product strategy, with a focus on building new product capabilities and driving innovation. | — | 0 |
| Data Manager II Data Manager II role focused on establishing and overseeing data management policies, practices, and controls, with a specific emphasis on data governance, data quality, and regulatory compliance. The role involves implementing AI governance standards, managing data risks, designing data quality frameworks, monitoring data quality, applying classification and metadata management, and defining data lifecycle requirements. It requires strong SQL and scripting skills, experience with data management tools, and stakeholder management. The role also involves coaching and supporting other data professionals. | — | 0 |