Senior Staff Engineer, Youtube Shorts Ranking, Core Modeling

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

Senior Staff Engineer role focused on building and optimizing large-scale machine learning models for YouTube Shorts ranking, including training infrastructure and quality evaluation. The role involves designing ML architecture and deploying recommendation systems models for retrieval, prediction, ranking, and embedding.

What you'd actually do

  1. Bring ideas to optimize shorts feed for better user experiences, new use cases, and overall product quality.
  2. Build large-scale machine learning models, training infrastructure and quality evaluation, design ML architecture.
  3. Work with data scientists, product managers, front-end engineers, user experience designers to improve product quality.

Skills

Required

  • software development
  • technical project strategy
  • ML design
  • industry-scale ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • design and architecture
  • testing/launching software products
  • building and deploying recommendation systems models
  • retrieval
  • prediction
  • ranking
  • embedding
  • architecture in different modeling domains

Nice to have

  • Master’s degree or PhD in Computer Science, Machine Learning, Computer Engineering, or a related highly technical field
  • data structures and algorithms
  • technical leadership role leading project teams and setting technical direction
  • working in a complex, matrixed organization involving cross-functional, or cross-business projects

What the JD emphasized

  • 8 years of experience in software development
  • 7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production.

Other signals

  • large-scale machine learning models
  • training infrastructure
  • quality evaluation
  • ML architecture
  • recommendation systems models
  • ranking
  • embedding