Principal Software Engineer I - Data & AI - Marketplace

Booking Booking · Hospitality · Amsterdam, Netherlands · Engineering

Principal Software Engineer focused on leading the technical vision and strategy for the AI Application Platform and intelligence products at Booking.com. This role involves defining engineering patterns, architectural guidelines, and the full lifecycle of AI/ML capabilities, from data to deployed models and business outcomes. It requires deep expertise in data and AI engineering, including data pipelines, feature stores, model serving, and experimentation, with a focus on scaling AI solutions across the company.

What you'd actually do

  1. Lead scoping and (re)design of various systems within the area, having a direct impact on the business, and on the experience of Booking.com's customers and partners.
  2. Define the engineering patterns and standards for building, evaluating, and operationalizing AI/ML capabilities across Marketplace — covering the full lifecycle from data to the deployed model to measurable business outcomes.
  3. Drive the technical direction of the AI Application Platform, ensuring it serves as a genuine accelerant for intelligence product development rather than another layer of complexity.
  4. Transform conceptual ideas into a specific set of artifacts (visualizations, list of capabilities etc.) by defining the scope, constraints, non-functional requirements, engaging and managing required stakeholders, identifying scenarios, and guiding principles.
  5. Serve as a tech lead on projects involving multiple teams across and beyond the business unit.

Skills

Required

  • Technical leadership
  • AI/ML engineering
  • Data engineering
  • System design
  • Architectural principles
  • SDLC
  • Stakeholder management
  • Cross-functional collaboration
  • Scalable and resilient application development

Nice to have

  • Hands-on development of proof of concepts
  • Evaluation of new technologies

What the JD emphasized

  • technical vision for the AI Application Platform
  • intelligence products
  • engineering patterns and standards for building, evaluating, and operationalizing AI/ML capabilities
  • full lifecycle from data to the deployed model to measurable business outcomes
  • technical direction of the AI Application Platform
  • intelligence product development
  • data pipelines and feature stores through to model serving, experimentation, and outcome measurement
  • intelligence engineering strategy

Other signals

  • AI Application Platform
  • intelligence products
  • ranking and recommendation engines
  • traveler context models
  • personalization
  • data pipelines
  • feature stores
  • model serving
  • experimentation
  • outcome measurement