Senior Software Engineer, Recommendations, Rankings, Predictions, Search Discover

Google Google · Big Tech · Mountain View, CA +1

Google is seeking a Senior Software Engineer to work on its AI-driven personalization and recommendation engine for Search and Discover. The role involves developing and testing new ranking algorithms and model features, analyzing experiment results, and designing/implementing recommendation systems models. The engineer will leverage ML infrastructure and contribute to architecture design, influencing what millions of users see daily.

What you'd actually do

  1. Develop and test new ranking algorithms and model features
  2. Analyze experiment results to measure impact and guide improvements.
  3. Collaborate with other engineers and researchers to implement new ideas.
  4. Contribute to the quality and reliability of the ranking systems. Explore new techniques to advance content understanding and personalization.
  5. Design and implement recommendation systems models across different domains, leverage ML infrastructure, and contribute to architecture design.

Skills

Required

  • software development
  • software design and architecture
  • building and deploying recommendation systems models
  • ML infrastructure
  • data analysis
  • data manipulation
  • statistical methods

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • data structures and algorithms
  • machine learning concepts
  • algorithms
  • best practices
  • building large language models
  • Python
  • C++
  • problem-solving
  • investigative skills
  • collaboration
  • communication
  • team-work skills

What the JD emphasized

  • 3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Other signals

  • Deep learning models
  • LLMs
  • personalization
  • recommendation engine
  • ranking paradigms