Machine Learning Engineer II

Expedia Expedia · Hospitality · Gurgaon, India

Machine Learning Engineer II at Expedia Group, focusing on the Distribution & Supply team. The role involves designing, implementing, deploying, and scaling ML solutions for the travel supply marketplace, improving pricing, surfacing, and overall platform performance. Responsibilities include data preprocessing, feature engineering, model evaluation, system design (LLD, API, data modeling), and integrating AI/ML capabilities into large-scale applications.

What you'd actually do

  1. Design and implement scalable machine learning solutions across multiple domains, ensuring technical rigor and robust architecture.
  2. Develop and deploy machine learning models, including data preprocessing, feature engineering, and model evaluation.
  3. Collaborate cross-functionally to integrate ML systems with existing products and services, promoting technical excellence and innovation.
  4. Drive system design, including low-level design (LLD), API design, and data modeling to support real-time and batch ML workflows.
  5. Safely integrate and operate AI/ML-enabled solutions that improve outcomes, leveraging modern AI/ML concepts and best practices.

Skills

Required

  • Python
  • Java
  • TensorFlow
  • PyTorch
  • scikit-learn
  • end-to-end machine learning projects
  • model deployment in production

Nice to have

  • operating machine learning solutions at scale
  • reliability
  • performance
  • maintainability
  • designing and implementing ML systems within multi-service environments
  • automated monitoring
  • testing
  • retraining pipelines for ML models
  • responsible AI practices
  • model fairness
  • interpretability
  • compliance within product environments

What the JD emphasized

  • Demonstrated ownership of end-to-end machine learning projects, from data exploration through model deployment in production.
  • operating machine learning solutions at scale, with an emphasis on reliability, performance, and maintainability.
  • integrating AI/ML capabilities into large-scale applications, ensuring responsible use of data and model outcomes.
  • responsible AI practices, including model fairness, interpretability, and compliance within product environments.

Other signals

  • design, deploy, and scale robust models
  • integrate ML systems with existing products and services
  • operate AI/ML-enabled solutions