Machine Learning Engineer I

Booking Booking · Hospitality · Amsterdam, Netherlands · ML Engineering

Machine Learning Engineer I at Booking.com in Amsterdam, Netherlands. This role focuses on developing production-grade ML systems for personalization at scale, understanding traveler preferences, and improving end-to-end experiences. Responsibilities include building ML systems, writing clean code, owning services, data governance, and evaluating architecture. Requires 2+ years of experience in ML applications, recommender systems, deep learning, information retrieval, MLOps, Python, and Spark.

What you'd actually do

  1. Develop production-grade ML systems, from models to features and pipelines, accounting for reliability, scalability, real-time requirements, monitoring and retraining.
  2. 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.
  3. Take full ownership of your services end to end by actively monitoring the systems health, performance and business impact.
  4. 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.
  5. Evaluate possible architecture solutions taking into account the business and technology requirements.

Skills

Required

  • Python
  • Spark
  • version control systems
  • English communication skills

Nice to have

  • Recommender Systems
  • Deep Learning
  • Information Retrieval
  • Computer Vision
  • Speech Recognition
  • Causal Inference
  • MLOps
  • large data sets
  • experimentation
  • scalability
  • optimization
  • data-driven product development
  • analytics
  • A/B testing

What the JD emphasized

  • production-grade ML systems
  • ownership
  • work independently and proactively
  • technical execution
  • continuous learning
  • data governance processes
  • ML governance processes
  • business and technology requirements
  • application of Machine Learning to business problems
  • large data sets
  • scalability and optimization
  • data-driven product development

Other signals

  • Develop production-grade ML systems
  • contribute significantly to ML projects
  • application of Machine Learning to business problems