Staff Software Engineer, Machine Learning, Youtube Shopping Recommendations

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

Staff Software Engineer for YouTube Shopping Recommendations, focusing on applied machine learning and recommendation systems to enhance the shopping experience within YouTube. The role involves technical leadership, driving viewer growth through ML algorithms, and collaborating across teams to build and deploy these systems at scale.

What you'd actually do

  1. Guide the development of personalized recommendations that shape the YouTube Shopping experience for millions of users.
  2. Serve as a technical leader, responsible for defining the team's charter, identifying new product opportunities, and executing projects that bridge gaps in the recommendations ecosystem.
  3. Drive viewer growth by designing and implementing machine learning (ML) technologies and algorithms to enhance Shopping recommendation quality across YouTube surfaces, including Home, Watch Next, and Shorts.
  4. Drive cross-team and cross-functional collaborations, working with the product manager for YouTube Shopping recommendations and cross-functional partners to build a team for billions of users.
  5. Gain experience across the recommendation system stack, working with and developing technologies such as model architecture and large language models (LLMs).

Skills

Required

  • software development
  • software products
  • software design and architecture
  • recommendation systems models
  • modeling domains
  • machine learning (ML) design and infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • C++
  • Python

Nice to have

  • technical leadership
  • large-scale interactive systems
  • TensorFlow
  • Jax
  • communication
  • collaboration

What the JD emphasized

  • building and deploying recommendation systems models in production
  • building architecture in modeling domains
  • machine learning (ML) design and infrastructure
  • large-scale interactive systems

Other signals

  • recommendation systems
  • machine learning
  • large scale systems
  • personalization